Generated Knowledge Prompting: A Complete Guide

Generated Knowledge Prompting (GKP) is a technique that improves language model performance by first generating relevant knowledge about a topic before using that knowledge to answer a question or complete a task. Instead of directly answering, the model first produces factual statements, background information, or contextual knowledge that becomes additional input for the final inference step. This two-stage approach leverages the model's parametric memory to surface relevant information that might otherwise remain latent during direct questioning.

The technique addresses a fundamental challenge in language model reasoning: models often possess relevant knowledge in their parameters but fail to activate or apply it when answering questions directly. By explicitly generating knowledge first, GKP creates a computational scaffold that primes the model with pertinent information, improving accuracy on tasks requiring world knowledge, commonsense reasoning, and factual understanding.

Category: Generated Knowledge Prompting belongs to knowledge-augmented and meta-cognitive prompting techniques. It's a self-elicitation approach that uses the model itself as a knowledge source before inference.

Type: Knowledge-based technique that enhances responses through explicit intermediate knowledge generation, combining aspects of retrieval (from parametric memory) and reasoning.

Scope: GKP includes generating factual statements, background context, relevant definitions, and domain-specific knowledge before answering. It excludes retrieval from external databases (that's RAG), step-by-step reasoning chains (that's CoT), and fine-tuning approaches.

Why This Exists

Core Problems Solved:

• Latent knowledge activation: Models possess knowledge but fail to surface it during direct questioning
• Commonsense reasoning gaps: Direct prompting often misses implicit world knowledge needed for correct answers
• Context insufficiency: Questions lack the background information needed for accurate inference
• Knowledge retrieval failures: Standard prompting doesn't activate relevant parametric knowledge
• Shallow reasoning: Models jump to conclusions without considering relevant factual context

Value Proposition:

• Accuracy: 7-10% zero-shot improvements, 14-20% gains over few-shot prompting on commonsense benchmarks
• Self-sufficiency: No external knowledge base or retrieval system required
• Flexibility: Works across diverse domains without task-specific training
• Adaptability: Knowledge generated on-the-fly based on the specific question
• Simplicity: Straightforward two-stage process without complex pipelines
• Transparency: Generated knowledge visible and auditable

Research Foundation

Seminal Work: Liu et al. (2022)

The paper "Generated Knowledge Prompting for Commonsense Reasoning" by Jiacheng Liu, Alisa Liu, Ximing Lu, Sean Welleck, Peter West, Ronan Le Bras, Yejin Choi, and Hannaneh Hajishirzi introduced this technique. Published at ACL 2022 (Annual Meeting of the Association for Computational Linguistics), this research demonstrated that language models can serve as flexible knowledge sources for improving their own reasoning.

Key Findings:

• NumerSense (numerical commonsense): State-of-the-art performance
• CommonsenseQA 2.0 (general commonsense): State-of-the-art performance
• QASC (scientific commonsense): State-of-the-art performance
• Critical insight: A model's predictions improve when using its own generated knowledge, demonstrating the importance of symbolic knowledge representation in neural reasoning processes

Core Innovation:

The research addressed an open question: whether incorporating external knowledge benefits commonsense reasoning while maintaining the flexibility of pretrained sequence models. The answer was affirmative—but with a twist. Instead of retrieving knowledge from external sources, the technique generates knowledge directly from the language model itself.

Research Contributions:

1. Demonstrated that language models contain sufficient knowledge to improve their own predictions
2. Showed that explicit knowledge statements outperform implicit knowledge activation
3. Proved the approach works without task-specific supervision or structured knowledge bases
4. Established that generated knowledge can outperform retrieved knowledge from Wikipedia or Google in certain scenarios

Evolution:

The technique built upon earlier work in knowledge-enhanced language models and self-elicitation. Prior approaches often required:

• Access to structured knowledge bases (ConceptNet, WordNet)
• Custom retrieval systems
• Task-specific fine-tuning for knowledge integration

GKP eliminated these dependencies by using the model's own parametric knowledge, making it more accessible and broadly applicable.

Follow-up Research:

• Analogical Prompting (2023): Extended the concept by generating relevant examples and analogies
• Knowledge-Augmented Chain-of-Thought (2023): Combined GKP principles with reasoning chains
• Recitation-Augmented Generation: Simplified variant generating knowledge inline with answers
• Self-Ask (2022): Related approach generating intermediate questions

Real-World Performance

Original Paper Results:

Zero-Shot Settings:

• 7-10% improvements across NumerSense, CommonsenseQA, and QASC benchmarks
• Demonstrated that even without examples, knowledge generation improves predictions

Comparison with Few-Shot Prompting:

• 14-20% improvements across commonsense reasoning tasks
• Generated knowledge outperformed standard few-shot examples

Comparison with Retrieved Knowledge:

• Generated knowledge outperformed loosely retrieved knowledge (Wikipedia, Google) by approximately 9%
• However, gold-standard domain-specific knowledge bases still performed better when available

Knowledge Quantity Analysis:

• Performance plateaus around 1-50 knowledge statements per question
• Most gains occur with any knowledge inclusion (even single statements help)
• Diminishing returns beyond moderate knowledge amounts

Domain-Specific Evidence:

Numerical Commonsense (NumerSense):

Questions requiring understanding of typical quantities (e.g., "A person has ___ legs")

• State-of-the-art accuracy
• Particularly effective for questions requiring world knowledge about quantities

Scientific Reasoning (QASC):

Multi-hop scientific questions requiring combining facts

• State-of-the-art results
• Knowledge generation helps surface relevant scientific principles

General Commonsense (CommonsenseQA 2.0):

Everyday reasoning about situations, objects, and behaviors

• Significant improvements over baselines
• Particularly effective for questions requiring implicit world knowledge

Comparative Performance:

| Technique | NumerSense | CommonsenseQA | QASC | | ---------------- | ---------- | ------------- | -------- | | Zero-shot | Baseline | Baseline | Baseline | | Few-shot | +5-8% | +5-8% | +5-8% | | GKP (zero-shot) | +7-10% | +7-10% | +7-10% | | GKP (few-shot) | +14-20% | +14-20% | +14-20% | | Retrieved (Wiki) | +5-12% | +5-12% | +5-12% | | Gold knowledge | +20-30% | +20-30% | +20-30% |

Production Considerations:

• Latency: Requires two LLM calls (knowledge generation + answer generation)
• Cost: Approximately 2x token usage compared to direct prompting
• Reliability: Knowledge quality varies; verification may be needed for critical applications

How It Works

Theoretical Foundation

Generated Knowledge Prompting is grounded in the distinction between parametric and symbolic knowledge representation. Language models encode vast knowledge in their parameters during pre-training, but this knowledge isn't always activated during inference. GKP bridges this gap by converting implicit parametric knowledge into explicit symbolic statements that can be directly utilized.

Core Insight: The act of generating knowledge statements forces the model to activate and articulate relevant information from its parameters. These explicit statements then become part of the prompt context, making the knowledge directly available for subsequent reasoning.

Fundamental Ideas:

Think of GKP as "thinking out loud" about what you know before answering. When asked "Is golf about getting a higher score than opponents?", a human might first recall: "Golf is played on courses with holes. The objective is to complete holes in the fewest strokes. Lower scores are better." This background knowledge makes the correct answer (No) obvious.

Conceptual Model:

Standard prompting: P(answer | question) Generated Knowledge Prompting: P(answer | question, generated_knowledge)

By conditioning on explicit knowledge, the model's answer distribution shifts toward responses consistent with the surfaced facts.

Why Self-Generated Knowledge Works:

1. Knowledge Activation: Generation forces retrieval from parametric memory
2. Attention Focusing: Explicit statements direct attention to relevant concepts
3. Context Enrichment: Additional tokens provide more signal for prediction
4. Disambiguation: Knowledge statements clarify implicit assumptions in questions

Assumptions:

• Models contain sufficient knowledge to generate relevant facts
• Generated knowledge will be more accurate than random
• Explicit knowledge improves prediction when integrated into context
• The two-stage process doesn't introduce significant error propagation

Where Assumptions Fail:

• Model lacks relevant knowledge (out-of-domain, recent events, specialized topics)
• Generated knowledge is incorrect (hallucination propagates to answer)
• Question doesn't benefit from additional context (simple retrieval tasks)
• Knowledge generation introduces more noise than signal

Trade-offs:

• Accuracy vs Speed: Two-stage process takes longer but improves quality
• Cost vs Quality: Additional API calls increase cost for better results
• Reliability vs Flexibility: Self-generated knowledge may hallucinate vs. verified external sources
• Simplicity vs Control: Automatic generation vs. curated knowledge selection

Execution Mechanism

Stage 1: Knowledge Generation

1. Prompt Construction:

• Create a prompt requesting relevant knowledge about the topic
• Use few-shot examples showing question → knowledge pairs
• Include 3-5 demonstrations of the expected knowledge format

2. Knowledge Statement Generation:

• Model generates M knowledge statements (typically 5-20)
• Each statement should be factually relevant to the question
• Statements are generated independently or in sequence

3. Knowledge Collection:

• Gather all generated knowledge statements
• Optionally filter or rank by relevance
• Prepare for integration stage

Stage 2: Knowledge Integration

1. Knowledge-Augmented Prompt Construction:

• Concatenate generated knowledge with original question
• Format: "Knowledge: [statements] Question: [original question]"
• Create multiple versions if using multiple knowledge statements

2. Answer Generation:

• Model generates answer conditioned on question + knowledge
• If multiple knowledge statements: generate answer for each
• Aggregate using probability-based selection or voting

3. Answer Selection:

• Select answer with highest prediction probability
• Or use majority voting across knowledge-augmented predictions
• Return final answer with optional confidence score

Cognitive Processes Triggered:

• Retrieval from memory: Explicit request activates stored knowledge
• Semantic association: Generating knowledge activates related concepts
• Contextual priming: Knowledge statements prime relevant neural pathways
• Verification grounding: Explicit facts provide anchors for reasoning

Is This Single-Pass or Multi-Stage?

GKP is inherently multi-stage:

• Minimum: Two stages (generate knowledge, then answer)
• Standard: Two stages with multiple knowledge samples
• Advanced: Multiple iterations with knowledge refinement

Completion Criteria:

• Knowledge generation: Fixed number of statements or until repetition
• Answer generation: Standard completion (EOS token, max tokens)
• Final selection: Highest probability or majority vote

Causal Mechanisms

Why This Improves Outputs:

1. Knowledge Surface Area Expansion:

Direct questions activate limited parametric knowledge. Explicit knowledge generation requests cast a wider net, surfacing facts that might be marginally relevant but prove crucial for correct answers.

2. Working Memory Augmentation:

Language models have limited "working memory" (context window). Generated knowledge statements extend effective working memory by explicitly encoding relevant information in the prompt.

3. Attention Redistribution:

With knowledge in the context, attention mechanisms can directly reference factual statements rather than implicitly reconstructing them from parameters.

4. Error Mode Correction:

Many errors stem from missing or incorrectly recalled facts. Explicit knowledge generation provides opportunity to surface correct information that might be overlooked in direct answering.

Cascading Effects:

• Relevant knowledge generated → Correct facts in context → Accurate reasoning → Correct answer
• Domain concepts activated → Related knowledge surfaces → Comprehensive understanding → Better inference

Feedback Loops:

• Positive: Good knowledge generation leads to correct answers, reinforcing the approach
• Negative: Hallucinated knowledge leads to confidently wrong answers, amplifying errors
• Self-reinforcing errors: Incorrect early knowledge can bias subsequent knowledge generation

Emergent Behaviors:

• Self-consistency: Multiple knowledge generations tend toward consistent facts
• Knowledge synthesis: Model sometimes combines partial facts into coherent knowledge
• Uncertainty surfacing: Generating knowledge can reveal when model is uncertain
• Domain transfer: Knowledge patterns transfer across related domains

Dominant Factors (ranked by impact):

1. Knowledge accuracy (40%): Correct facts most critical for improvement
2. Knowledge relevance (30%): Generated facts must relate to the question
3. Integration quality (15%): How well knowledge is incorporated into answering
4. Question complexity (10%): Benefits scale with question difficulty
5. Model capability (5%): Larger models generate better knowledge

Structure and Components

Essential Components

Knowledge Generation Prompt:

1. Instruction: "Generate facts/knowledge about [topic]"
2. Few-shot demonstrations: 3-5 examples of question → knowledge pairs
3. Format specification: How knowledge should be structured
4. Question placeholder: Where new question is inserted
5. Generation trigger: Signal to begin knowledge output

Knowledge Integration Prompt:

1. Knowledge section: Generated facts clearly marked
2. Question section: Original question clearly separated
3. Answer instruction: How to use knowledge for answering
4. Format specification: Expected answer format

Required vs Optional:

| Component | Required | Optional | | -------------------------------- | ------------------------ | -------- | | Knowledge generation instruction | Yes | - | | Few-shot knowledge examples | No (helps significantly) | Yes | | Question for knowledge | Yes | - | | Knowledge-question integration | Yes | - | | Answer format specification | No | Yes | | Multiple knowledge samples | No | Yes | | Probability-based selection | No | Yes |

Design Principles

Linguistic Patterns:

• Declarative statements: "X is Y", "X has property Z"
• Factual framing: "It is known that...", "Generally, X..."
• Definitional patterns: "X refers to...", "X is defined as..."
• Relational patterns: "X is related to Y through Z"
• Quantitative patterns: "X typically has N properties"

Cognitive Principles Leveraged:

• Priming: Knowledge statements activate related concepts
• Elaborative encoding: Generating knowledge deepens processing
• Retrieval practice: Actively generating improves recall
• Contextual cueing: Knowledge provides cues for answer retrieval
• Semantic spreading: Activated concepts spread to related ideas

Core Design Principles:

• Relevance: Generate knowledge specifically relevant to the question
• Accuracy: Prioritize factual correctness over quantity
• Clarity: Knowledge should be unambiguous and self-contained
• Diversity: Multiple knowledge statements should cover different aspects
• Separation: Clear distinction between knowledge and question

Structural Patterns

Minimal Pattern (Single-Prompt):

Generate 3 facts about [topic], then answer the question.

Topic: Golf scoring
Question: Is golf about getting a higher score than opponents?

Facts:
1. [Model generates fact 1]
2. [Model generates fact 2]
3. [Model generates fact 3]

Based on these facts, the answer is: [Model generates answer]

Standard Pattern (Two-Stage):

Stage 1 - Knowledge Generation:

Generate knowledge that would help answer questions about the topic.

Input: What is the capital of Australia?
Knowledge: Australia is a country in the Southern Hemisphere. The capital of Australia is Canberra. Canberra was chosen as a compromise between Sydney and Melbourne.

Input: How many legs does a spider have?
Knowledge: Spiders are arachnids, not insects. Arachnids have 8 legs. Spiders use their legs for walking, building webs, and catching prey.

Input: [New question]
Knowledge:

Stage 2 - Answer with Knowledge:

Use the following knowledge to answer the question.

Knowledge: [Generated knowledge from Stage 1]

Question: [Original question]

Answer:

Advanced Pattern (Multiple Knowledge + Selection):

Stage 1 - Generate Multiple Knowledge Sets:

# Generate M different knowledge completions with temperature > 0
knowledge_1 = generate_knowledge(question, temperature=0.7)
knowledge_2 = generate_knowledge(question, temperature=0.7)
...
knowledge_M = generate_knowledge(question, temperature=0.7)

Stage 2 - Score Each Knowledge-Answer Pair:

# For each knowledge set, generate answer and compute probability
for knowledge in knowledge_sets:
    augmented_prompt = f"Knowledge: {knowledge}\nQuestion: {question}"
    answer, probability = generate_answer_with_prob(augmented_prompt)
    candidates.append((answer, probability))

Stage 3 - Select Best Answer:

# Select answer with highest probability
best_answer = max(candidates, key=lambda x: x[1])

Reasoning Patterns Used:

• Retrieval then inference: Generate knowledge (retrieval), then answer (inference)
• Ensemble reasoning: Multiple knowledge samples, aggregate answers
• Probabilistic selection: Choose answer maximizing prediction probability
• Explicit grounding: Answers must align with generated knowledge

Modifications for Scenarios

High Ambiguity Questions:

• Generate more diverse knowledge (higher temperature)
• Include definitional knowledge to clarify terms
• Generate knowledge addressing multiple interpretations
• Use ensemble approach with voting

Domain-Specific Applications:

• Include domain-specific examples in few-shot knowledge generation
• Request domain terminology and principles
• Tailor knowledge format to domain conventions
• Consider domain-specific verification

Complex Multi-Part Questions:

• Generate knowledge for each part separately
• Synthesize knowledge before answering
• Use structured knowledge (bullet points, categories)
• Chain knowledge generation for dependent parts

Time-Sensitive Questions:

• Acknowledge knowledge cutoff limitations
• Generate knowledge about general principles (more stable)
• Flag potential outdatedness in answer
• Consider combining with retrieval for recent information

When Boundary Conditions Arise:

• Token limits: Generate concise knowledge, prioritize relevance
• Latency constraints: Use single-stage approach, fewer knowledge samples
• Unknown topics: Generate what is known, acknowledge uncertainty
• Conflicting knowledge: Include multiple perspectives, note disagreement

Applications and Task Selection

General Applications

Commonsense Reasoning:

• Everyday knowledge questions (CommonsenseQA)
• Physical world understanding (size, weight, properties)
• Social reasoning (intentions, emotions, norms)
• Temporal reasoning (sequences, durations)
• Causal reasoning (cause-effect relationships)

Factual Question Answering:

• Trivia and knowledge questions
• Scientific facts and principles
• Historical information
• Geographic knowledge
• Definitional queries

Numerical Reasoning:

• Questions about typical quantities (NumerSense)
• Order of magnitude reasoning
• Statistical common knowledge
• Unit conversions and comparisons

Classification with World Knowledge:

• Sentiment analysis requiring context understanding
• Topic classification needing domain knowledge
• Intent detection with background information
• Entity classification with attribute knowledge

Text Generation Enhancement:

• Blog posts with factual grounding
• Reports requiring background research
• Educational content with accurate information
• Documentation with domain context

Domain-Specific Applications

Scientific Domains:

• Biology: Species characteristics, biological processes
• Chemistry: Compound properties, reaction principles
• Physics: Physical laws, phenomena explanations
• Earth Science: Geographic facts, environmental knowledge

Results: QASC benchmark showed significant improvements for multi-hop scientific reasoning.

Healthcare (with caveats):

• Medical terminology clarification
• General health knowledge (not diagnosis)
• Anatomy and physiology basics
• Medication general information

Important: Generated knowledge should not replace verified medical sources; use for educational context only.

Business and Finance:

• Industry terminology and concepts
• Economic principles
• Market general knowledge
• Organizational concepts

Legal (educational context):

• Legal terminology definitions
• General legal concepts
• Procedural knowledge
• Jurisdiction basics

Education:

• Subject matter background
• Concept explanations
• Prerequisite knowledge activation
• Study material enhancement

Creative Applications:

• World-building background for fiction
• Character knowledge for dialogue
• Setting details for descriptions
• Research context for writing

Unconventional Applications:

• Game NPCs: Characters with consistent world knowledge
• Customer support: Product knowledge for better responses
• Code generation: Domain context for appropriate implementations
• Translation: Cultural knowledge for better localization

Selection Framework

Problem Characteristics Favoring GKP:

• Knowledge dependency: Answer requires factual background
• Commonsense gaps: Direct prompting misses implicit knowledge
• Multi-fact synthesis: Answer requires combining multiple pieces of information
• Context insufficiency: Question alone doesn't provide enough information
• Domain breadth: Requires knowledge across multiple areas

Optimized Scenarios:

• Commonsense reasoning tasks
• Factual question answering
• Classification requiring world knowledge
• Text generation needing accurate context
• Educational applications

NOT Recommended For:

• Simple retrieval: Single-fact questions don't need knowledge generation
• Reasoning-heavy tasks: Chain-of-Thought better for multi-step logic
• Recent information: Model's knowledge cutoff limits accuracy
• Highly specialized domains: External retrieval (RAG) preferable
• Real-time applications: Two-stage latency unacceptable
• When external sources available: Verified retrieval more reliable

Model Requirements:

• Minimum: Models with substantial world knowledge (GPT-3.5+, Claude Haiku+)
• Recommended: GPT-4, Claude 3+, Gemini Pro, Llama 70B+
• Optimal: Models with broad factual knowledge and good instruction following
• Not suitable: Small models (<7B), specialized models without general knowledge

Context/Resource Requirements:

• Knowledge generation: 200-500 tokens for few-shot examples + 100-300 tokens output
• Knowledge integration: Generated knowledge (100-500 tokens) + question + answer
• Total typical: 500-1500 tokens per request (both stages combined)
• API calls: Minimum 2 calls (generation + answer), potentially M+1 for ensemble

Latency Considerations:

• Single-stage (combined): 2-4 seconds
• Two-stage (sequential): 4-8 seconds
• Ensemble (M samples): M × 2-3 seconds + voting
• Critical: Approximately 2x latency vs direct prompting

Cost Implications:

One-time Costs:

• Developing few-shot examples: 1-2 hours
• Testing and validation: 1-2 hours
• Prompt optimization: 1-3 hours

Per-Request Costs:

• Approximately 2x token usage vs direct prompting
• Knowledge generation: ~300-500 tokens
• Answer generation: ~200-400 tokens
• Ensemble multiplies costs by sample count

Cost-Quality Trade-offs:

• Single knowledge: Lower cost, moderate improvement
• Multiple knowledge (M=5): Higher cost, better improvement
• Ensemble with voting: Highest cost, most robust

When to Use vs NOT Use:

Use When:

• Task involves commonsense or world knowledge
• Direct prompting produces factually incorrect answers
• Model has relevant knowledge but doesn't activate it
• Quality improvements justify latency/cost increase
• External retrieval not available or not preferred

Do NOT Use When:

• Simple factual retrieval (use direct prompting)
• Complex reasoning needed (use Chain-of-Thought)
• Recent or specialized information required (use RAG)
• Latency critical (<2 seconds required)
• High-stakes requiring verified facts
• Model lacks relevant domain knowledge

When to Escalate:

To Chain-of-Thought:

• Problem requires multi-step reasoning, not just knowledge
• Logical deduction needed beyond factual recall
• Mathematical or symbolic manipulation required

To RAG (Retrieval-Augmented Generation):

• Recent information needed (after training cutoff)
• Highly specialized domain knowledge
• Verified/authoritative sources required
• Large knowledge base available

To Hybrid (GKP + CoT):

• Complex problems requiring both knowledge and reasoning
• Multi-hop questions with factual and logical components
• Domain reasoning with specialized knowledge

Variant Selection:

• Single-stage GKP: Quick applications, moderate accuracy needs
• Two-stage GKP: Standard applications, better accuracy
• Ensemble GKP: High-stakes, accuracy-critical applications
• GKP + CoT hybrid: Complex reasoning with knowledge requirements

Implementation

Implementation Steps

Step 1: Task Analysis

• Identify if task benefits from additional knowledge
• Determine what types of knowledge would help
• Assess if model likely contains relevant knowledge
• Decide on single-stage vs two-stage approach

Step 2: Knowledge Generation Prompt Design

• Create instruction for knowledge generation
• Develop 3-5 few-shot examples showing:
  • Input question/topic
  • Expected knowledge format
  • Diverse knowledge types (facts, definitions, relationships)
• Test knowledge quality on sample inputs

Step 3: Knowledge Integration Prompt Design

• Design format for presenting knowledge with question
• Create clear separation between knowledge and question
• Include instruction on using knowledge for answering
• Test integration on sample knowledge + questions

Step 4: Pipeline Implementation

• Implement knowledge generation call
• Implement knowledge integration call
• Add error handling for failed generations
• Implement answer extraction logic

Step 5: Testing and Validation

• Test on 20-30 representative examples
• Measure accuracy improvement vs baseline
• Analyze failure cases
• Iterate on prompts based on failures

Step 6: Optimization (Optional)

• Implement ensemble approach if needed
• Add knowledge quality filtering
• Optimize token usage
• Implement caching for repeated queries

Platform-Specific Implementations

OpenAI API (Python):

import openai
from typing import List, Dict

def generate_knowledge(question: str, num_samples: int = 1) -> List[str]:
    """Generate knowledge statements for a question."""

    knowledge_prompt = """Generate relevant knowledge that would help answer the question.

Input: What is the largest planet in our solar system?
Knowledge: Jupiter is the largest planet in our solar system. It is a gas giant with a mass more than twice that of all other planets combined. Jupiter has a diameter of about 139,820 km.

Input: Do penguins fly?
Knowledge: Penguins are flightless birds. They have evolved flippers instead of wings for swimming. Penguins are excellent swimmers and can dive to great depths. Their bodies are adapted for aquatic life rather than aerial flight.

Input: {question}
Knowledge:"""

    knowledge_list = []

    for _ in range(num_samples):
        response = openai.chat.completions.create(
            model="gpt-4",
            messages=[
                {"role": "user", "content": knowledge_prompt.format(question=question)}
            ],
            temperature=0.7,  # Some diversity for multiple samples
            max_tokens=300
        )
        knowledge_list.append(response.choices[0].message.content)

    return knowledge_list


def answer_with_knowledge(question: str, knowledge: str) -> Dict[str, any]:
    """Generate answer using provided knowledge."""

    answer_prompt = f"""Use the following knowledge to answer the question accurately.

Knowledge: {knowledge}

Question: {question}

Answer:"""

    response = openai.chat.completions.create(
        model="gpt-4",
        messages=[
            {"role": "user", "content": answer_prompt}
        ],
        temperature=0.3,  # Lower temperature for consistent answers
        max_tokens=200,
        logprobs=True,
        top_logprobs=1
    )

    answer = response.choices[0].message.content

    # Calculate average log probability as confidence score
    logprobs = response.choices[0].logprobs
    if logprobs and logprobs.content:
        avg_logprob = sum(t.logprob for t in logprobs.content) / len(logprobs.content)
    else:
        avg_logprob = None

    return {
        "answer": answer,
        "confidence": avg_logprob,
        "knowledge_used": knowledge
    }


def generated_knowledge_prompting(
    question: str,
    num_knowledge_samples: int = 5
) -> Dict[str, any]:
    """Complete GKP pipeline with ensemble selection."""

    # Stage 1: Generate multiple knowledge samples
    knowledge_samples = generate_knowledge(question, num_knowledge_samples)

    # Stage 2: Generate answers for each knowledge sample
    candidates = []
    for knowledge in knowledge_samples:
        result = answer_with_knowledge(question, knowledge)
        candidates.append(result)

    # Stage 3: Select best answer (highest confidence)
    if all(c["confidence"] is not None for c in candidates):
        best = max(candidates, key=lambda x: x["confidence"])
    else:
        # Fallback to first if no confidence scores
        best = candidates[0]

    return {
        "answer": best["answer"],
        "knowledge": best["knowledge_used"],
        "all_candidates": candidates
    }


# Example usage
if __name__ == "__main__":
    question = "Is it true that in golf, players try to get a higher point total than others?"

    result = generated_knowledge_prompting(question, num_knowledge_samples=3)

    print(f"Question: {question}")
    print(f"Generated Knowledge: {result['knowledge']}")
    print(f"Answer: {result['answer']}")

Anthropic Claude API:

import anthropic

client = anthropic.Anthropic()

def claude_gkp(question: str) -> dict:
    """Generated Knowledge Prompting with Claude."""

    # Stage 1: Knowledge Generation
    knowledge_response = client.messages.create(
        model="claude-3-5-sonnet-20241022",
        max_tokens=400,
        messages=[{
            "role": "user",
            "content": f"""Generate 3-5 relevant facts that would help answer this question.

Question: {question}

Facts:"""
        }]
    )

    knowledge = knowledge_response.content[0].text

    # Stage 2: Answer with Knowledge
    answer_response = client.messages.create(
        model="claude-3-5-sonnet-20241022",
        max_tokens=300,
        messages=[{
            "role": "user",
            "content": f"""Based on the following knowledge, answer the question.

Knowledge:
{knowledge}

Question: {question}

Answer:"""
        }]
    )

    return {
        "knowledge": knowledge,
        "answer": answer_response.content[0].text
    }

LangChain Implementation:

from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate, FewShotChatMessagePromptTemplate
from langchain_core.output_parsers import StrOutputParser

# Initialize LLM
llm = ChatOpenAI(model="gpt-4", temperature=0.5)

# Knowledge generation examples
knowledge_examples = [
    {
        "question": "Can camels survive without water for months?",
        "knowledge": "Camels are adapted to desert environments. They can survive without drinking water for about 7-10 days in hot weather, not months. They store fat in their humps, not water. Their bodies are efficient at conserving water through specialized kidneys and minimal sweating."
    },
    {
        "question": "Is the Great Wall of China visible from space?",
        "knowledge": "The Great Wall of China is about 13,000 miles long but only 15-30 feet wide. From low Earth orbit, it is not easily visible to the naked eye due to its narrow width. Astronauts have reported difficulty seeing it without aid. The claim about visibility from space is a common misconception."
    }
]

# Create few-shot template for knowledge generation
example_prompt = ChatPromptTemplate.from_messages([
    ("human", "Generate knowledge for: {question}"),
    ("ai", "{knowledge}")
])

few_shot_prompt = FewShotChatMessagePromptTemplate(
    example_prompt=example_prompt,
    examples=knowledge_examples
)

# Full knowledge generation prompt
knowledge_prompt = ChatPromptTemplate.from_messages([
    ("system", "You are a knowledgeable assistant. Generate relevant factual knowledge to help answer questions."),
    few_shot_prompt,
    ("human", "Generate knowledge for: {question}")
])

# Answer generation prompt
answer_prompt = ChatPromptTemplate.from_messages([
    ("system", "Use the provided knowledge to answer the question accurately and concisely."),
    ("human", """Knowledge: {knowledge}

Question: {question}

Answer:""")
])

# Create chains
knowledge_chain = knowledge_prompt | llm | StrOutputParser()
answer_chain = answer_prompt | llm | StrOutputParser()

def langchain_gkp(question: str) -> dict:
    """GKP implementation using LangChain."""

    # Generate knowledge
    knowledge = knowledge_chain.invoke({"question": question})

    # Generate answer using knowledge
    answer = answer_chain.invoke({
        "question": question,
        "knowledge": knowledge
    })

    return {
        "knowledge": knowledge,
        "answer": answer
    }

Single-Prompt Variant:

def single_prompt_gkp(question: str) -> dict:
    """Simplified single-prompt GKP approach."""

    prompt = f"""First, generate relevant knowledge about the topic, then answer the question.

Question: {question}

Step 1 - Relevant Knowledge:
Generate 3-4 facts that would help answer this question.

Step 2 - Answer:
Based on the knowledge above, provide your answer.

Response:"""

    response = openai.chat.completions.create(
        model="gpt-4",
        messages=[{"role": "user", "content": prompt}],
        temperature=0.3,
        max_tokens=500
    )

    return {
        "full_response": response.choices[0].message.content
    }

Configuration

Key Parameters:

Temperature (Knowledge Generation):

• 0.3-0.5: Consistent, focused knowledge (single-sample approach)
• 0.7-0.9: Diverse knowledge (ensemble approach)
• Recommendation: 0.7 for ensemble, 0.4 for single-sample

Temperature (Answer Generation):

• 0.0-0.3: Consistent answers (recommended)
• Higher: Only if creative responses desired
• Recommendation: 0.2-0.3 for factual tasks

Max Tokens:

• Knowledge generation: 200-400 tokens (adjust for domain)
• Answer generation: 100-300 tokens (task-dependent)
• Buffer: Add 20% for variation

Number of Knowledge Samples:

• Minimum: 1 (single-sample approach)
• Standard: 3-5 (good balance)
• High-stakes: 5-10 (more robust)
• Diminishing returns: Beyond 10-15 samples

Few-Shot Examples:

• Minimum: 2 examples (establishes pattern)
• Optimal: 3-5 examples (best performance)
• Maximum: 7-8 examples (context limits)

Model-Specific Settings:

GPT-4:

• Knowledge temp: 0.6-0.7
• Answer temp: 0.2
• Works well with structured examples
• Good at following knowledge format

Claude:

• Knowledge temp: 0.5-0.7
• Answer temp: 0.2
• Responds well to conversational instructions
• Clear knowledge-question separation important

Gemini:

• Knowledge temp: 0.6
• Answer temp: 0.2
• Benefits from explicit formatting
• Good multi-shot learning

Open-source (Llama 70B+):

• Knowledge temp: 0.5-0.6
• Answer temp: 0.1-0.2
• More examples needed (5-7)
• Simpler knowledge format preferred

Best Practices and Workflow

Do:

• Use clear, specific instructions for knowledge generation
• Include diverse few-shot examples covering different knowledge types
• Separate knowledge and question clearly in integration prompt
• Validate knowledge quality on sample outputs
• Use ensemble approach for important applications
• Monitor for hallucinated knowledge
• Test baseline performance before adding GKP

Don't:

• Trust generated knowledge without verification for high-stakes tasks
• Use GKP when external verified sources are available
• Apply to simple questions that don't need knowledge augmentation
• Assume knowledge is always factually correct
• Use excessive knowledge samples (diminishing returns)
• Ignore latency and cost implications
• Apply to domains where model lacks knowledge

Knowledge Generation Tips:

• Request specific types of knowledge (facts, definitions, relationships)
• Include format examples (bullet points, sentences)
• Specify knowledge quantity (3-5 facts)
• Request relevant knowledge, not comprehensive knowledge
• Consider asking for knowledge from multiple perspectives

Knowledge Integration Tips:

• Label knowledge section clearly
• Instruct model to use knowledge for answering
• Don't overwhelm with excessive knowledge
• Keep question prominent in the prompt
• Request answer format explicitly

Workflow:

1. Analyze Task (5-10 min)
   - Does task benefit from additional knowledge?
   - What types of knowledge would help?
   - Is model likely to have relevant knowledge?

2. Design Prompts (30-60 min)
   - Create knowledge generation prompt with examples
   - Create knowledge integration prompt
   - Define expected output formats

3. Initial Testing (30 min)
   - Test on 5-10 examples
   - Evaluate knowledge quality
   - Check answer accuracy vs baseline

4. Iterate (30-60 min)
   - Refine examples based on failures
   - Adjust instructions
   - Test improvements

5. Validation (30-60 min)
   - Test on 20-30 held-out examples
   - Calculate accuracy improvement
   - Analyze failure modes

6. Deployment
   - Implement production pipeline
   - Add monitoring for knowledge quality
   - Set up fallback mechanisms

Debugging Decision Tree

Generated Knowledge is Irrelevant:

Root Cause: Few-shot examples don't demonstrate relevance, instruction unclear

Solutions:

• Add more focused examples showing relevant knowledge
• Include explicit instruction: "Generate knowledge directly relevant to answering this question"
• Add negative examples showing what not to generate
• Increase example diversity

Generated Knowledge Contains Errors:

Root Cause: Model hallucinating, knowledge outside training data

Solutions:

• Add instruction: "Only generate factual information you are confident about"
• Include verification step: "Verify each fact before including"
• Reduce knowledge quantity (fewer, more certain facts)
• Lower temperature for more conservative generation
• Consider fallback to retrieval for critical facts

Answer Ignores Generated Knowledge:

Root Cause: Knowledge not integrated properly, answer section unclear

Solutions:

• Strengthen integration instruction: "Based specifically on the knowledge above..."
• Move knowledge closer to question in prompt
• Add explicit reference requirement: "Cite which facts support your answer"
• Use clearer delimiters between sections

Inconsistent Answers Across Knowledge Samples:

Root Cause: Knowledge variations leading to different answers

Solutions:

• Use voting across multiple knowledge-answer pairs
• Reduce knowledge generation temperature for consistency
• Filter knowledge for quality before integration
• Use ensemble approach with majority voting

Performance Worse Than Baseline:

Root Cause: Task doesn't benefit from knowledge, bad knowledge quality, overhead not justified

Solutions:

• Verify task actually benefits from additional knowledge
• Check knowledge quality (is it helping or hurting?)
• Test without GKP on problematic examples
• Consider alternative approaches (CoT, RAG)
• Accept that some tasks don't benefit from GKP

High Latency/Cost:

Root Cause: Two-stage process, multiple samples

Solutions:

• Use single-prompt variant for latency-sensitive applications
• Reduce number of knowledge samples
• Cache knowledge for repeated similar queries
• Use smaller model for knowledge generation
• Implement async processing

Format Violations:

Root Cause: Unclear format instructions, inconsistent examples

Solutions:

• Add explicit format templates
• Include format examples in knowledge generation prompt
• Use structured output parsing
• Add format validation and retry logic

Common Mistakes:

• Generating too much knowledge (overwhelming the context)
• Not testing against baseline (assuming GKP always helps)
• Using GKP for reasoning tasks (CoT is better)
• Ignoring knowledge quality (hallucinations propagate)
• One-size-fits-all approach (different tasks need different knowledge types)
• Not verifying critical facts externally

Testing and Optimization

Validation Strategy

Test Set Design:

Create 30-50 test examples covering:

• Common cases (50%): Typical questions in your domain
• Edge cases (30%): Unusual or boundary questions
• Known failures (20%): Questions direct prompting gets wrong

Test Coverage:

• Happy path: Well-formed questions where GKP should help
• No-benefit cases: Questions where knowledge doesn't help
• Out-of-domain: Questions outside model's knowledge
• Ambiguous: Questions with multiple valid interpretations
• Adversarial: Questions designed to elicit hallucinations

Validation Methods:

• Baseline comparison: Always measure GKP vs direct prompting
• Holdout validation: Keep test set separate from development
• Human evaluation: Judge knowledge quality and answer accuracy
• A/B testing: Compare variants in production

Knowledge Quality Assessment:

Evaluate generated knowledge on:

• Accuracy: Are facts correct?
• Relevance: Do facts help answer the question?
• Coverage: Are important aspects covered?
• Conciseness: Is knowledge appropriately brief?

Quality Metrics

Task-Specific:

• Factual QA: Exact match, F1 score
• Classification: Accuracy, precision, recall
• Multiple choice: Accuracy, selection confidence
• Generation: BLEU, ROUGE, human evaluation

Knowledge Quality:

• Factual accuracy: % of generated facts that are correct
• Relevance score: % of facts useful for answering
• Hallucination rate: % of facts that are fabricated
• Diversity: Coverage of different relevant aspects

System Metrics:

• Latency: Total time for GKP vs baseline
• Token usage: Total tokens for GKP vs baseline
• Cost per query: API costs for full pipeline
• Improvement ratio: (GKP accuracy - baseline) / baseline

Comparison Framework:

def evaluate_gkp(test_set, baseline_fn, gkp_fn):
    """Compare GKP performance against baseline."""

    baseline_correct = 0
    gkp_correct = 0

    for example in test_set:
        question = example["question"]
        expected = example["answer"]

        # Baseline prediction
        baseline_answer = baseline_fn(question)
        if evaluate_answer(baseline_answer, expected):
            baseline_correct += 1

        # GKP prediction
        gkp_result = gkp_fn(question)
        if evaluate_answer(gkp_result["answer"], expected):
            gkp_correct += 1

    n = len(test_set)
    print(f"Baseline accuracy: {baseline_correct/n:.2%}")
    print(f"GKP accuracy: {gkp_correct/n:.2%}")
    print(f"Improvement: {(gkp_correct - baseline_correct)/n:.2%}")

    return {
        "baseline": baseline_correct / n,
        "gkp": gkp_correct / n,
        "improvement": (gkp_correct - baseline_correct) / n
    }

Optimization Techniques

Token Efficiency:

Knowledge Compression:

• Request concise knowledge: "Generate 3 brief, relevant facts"
• Use bullet points instead of paragraphs
• Remove filler phrases from examples
• Limit knowledge to most relevant facts
• Typical savings: 20-30% tokens

Prompt Compression:

• Minimize example count while maintaining quality
• Use shorter example questions/knowledge
• Remove redundant instructions
• Typical savings: 10-20% tokens

Answer Compression:

• Request concise answers
• Use structured output formats
• Extract only essential information
• Post-process for brevity

Cost-Performance Trade-offs:

| Approach | Token Cost | Latency | Accuracy Gain | | -------------------- | ---------- | ------- | ------------- | | Single knowledge | 1.5x | 1.5x | +5-10% | | 3 knowledge samples | 3x | 2x | +10-15% | | 5 knowledge samples | 4x | 2.5x | +12-18% | | 10 knowledge samples | 7x | 4x | +15-20% |

Caching Strategies:

• Cache knowledge for repeated similar queries
• Cache few-shot examples (don't regenerate)
• Cache answers for identical questions
• Use semantic similarity for cache hits

Consistency Techniques:

• Lower temperature for knowledge generation (0.3-0.5)
• Use voting across multiple samples
• Filter out inconsistent knowledge
• Verify knowledge against each other

Iteration Criteria:

• Stop if accuracy reaches target threshold
• Stop if improvements <2% for 2 iterations
• Maximum 5 iterations (diminishing returns)
• Always compare against baseline

Experimentation

A/B Testing:

import random
from scipy import stats

def ab_test_gkp(test_questions, n_iterations=3):
    """A/B test GKP vs baseline."""

    results = {"baseline": [], "gkp": []}

    for question, expected in test_questions:
        # Randomly assign to A or B
        if random.random() < 0.5:
            answer = baseline_prompt(question)
            results["baseline"].append(evaluate(answer, expected))
        else:
            answer = gkp_prompt(question)
            results["gkp"].append(evaluate(answer, expected))

    # Statistical comparison
    t_stat, p_value = stats.ttest_ind(
        results["baseline"],
        results["gkp"]
    )

    return {
        "baseline_accuracy": sum(results["baseline"]) / len(results["baseline"]),
        "gkp_accuracy": sum(results["gkp"]) / len(results["gkp"]),
        "p_value": p_value,
        "significant": p_value < 0.05
    }

Variant Comparison:

Test variations systematically:

• Number of knowledge samples (1, 3, 5, 7)
• Temperature settings (0.3, 0.5, 0.7)
• Few-shot example count (2, 3, 5)
• Knowledge format (bullets vs prose)
• Integration prompt styles

Handling Randomness:

• Run each configuration 3-5 times
• Report mean and standard deviation
• Use paired comparisons (same questions)
• Set random seeds for reproducibility
• Statistical significance testing

Limitations and Constraints

Known Limitations

1. Hallucination Propagation (Primary Risk):

The most significant limitation. If the model generates incorrect knowledge in Stage 1, this false information is treated as true in Stage 2, leading to confidently wrong answers.

Why: Language models can generate plausible-sounding but false statements. Unlike retrieval from verified sources, generated knowledge has no external validation.

Impact:

• Errors compound through the pipeline
• Wrong answers delivered with high confidence
• Harder to detect than direct prompting errors
• Particularly problematic for factual questions

Cannot be fully overcome: Inherent to using model's parametric knowledge without external verification.

Mitigation:

• Verify critical facts externally
• Use lower temperature for more conservative knowledge
• Request uncertainty acknowledgment
• Implement knowledge quality filtering
• Combine with retrieval for high-stakes applications

2. Knowledge Recency Limitations:

Models can only generate knowledge from their training data, which has a cutoff date.

Impact:

• Outdated information for recent events
• Wrong answers for evolving topics
• Cannot access new research, news, or changes

Mitigation:

• Use retrieval (RAG) for time-sensitive queries
• Acknowledge knowledge cutoff in responses
• Focus on stable, timeless knowledge

3. Domain Knowledge Gaps:

Models have uneven knowledge across domains—strong in common topics, weak in specialized areas.

Impact:

• Poor performance on specialized domains
• Increased hallucination in unfamiliar areas
• Inconsistent results across topics

Mitigation:

• Use domain-specific retrieval for specialized tasks
• Test domain knowledge before deploying GKP
• Consider fine-tuned models for specific domains

4. Computational Overhead:

Two-stage process approximately doubles latency and cost.

Impact:

• 2x token usage
• 2x API costs
• 1.5-2x latency
• May not be acceptable for high-throughput applications

Cannot be overcome: Inherent to the two-stage design.

Mitigation:

• Single-prompt variant for latency-sensitive cases
• Caching for repeated queries
• Batch processing where possible
• Use smaller models for knowledge generation

5. No Reasoning Capability:

GKP generates knowledge, not reasoning chains. Complex problems requiring multi-step logic won't benefit.

Impact:

• Won't help with mathematical reasoning
• Not suitable for logical deduction
• Doesn't improve step-by-step problem solving

Mitigation:

• Use Chain-of-Thought for reasoning tasks
• Combine GKP with CoT for knowledge + reasoning
• Apply GKP only to knowledge-dependent tasks

6. Quality Variability:

Knowledge quality varies significantly across queries, topics, and model runs.

Impact:

• Inconsistent performance
• Some queries benefit greatly, others not at all
• Hard to predict when GKP will help

Mitigation:

• Ensemble approach with multiple samples
• Knowledge quality filtering
• Fallback mechanisms for poor knowledge
• A/B testing to identify beneficial use cases

Edge Cases

Questions with No Relevant Knowledge:

Problem: Some questions don't benefit from additional knowledge

Detection: Generated knowledge generic or tangential

Handling:

• Fall back to direct prompting
• Detect low-relevance knowledge and skip integration
• Test baseline performance for comparison

Contradictory Generated Knowledge:

Problem: Model generates conflicting facts

Detection: Statements that contradict each other

Handling:

• Flag contradictions for review
• Use voting to identify majority position
• Request reconciliation: "Resolve any contradictions"
• Filter out contradicting statements

Knowledge Beyond Model's Confidence:

Problem: Model generates knowledge about unfamiliar topics

Detection: Hallucinations, hedged language, inconsistency

Handling:

• Request confidence indicators
• Lower temperature for uncertain topics
• Detect uncertainty markers in generated knowledge
• Fall back to retrieval for unfamiliar domains

Very Long or Complex Questions:

Problem: Question too complex for single knowledge generation

Detection: Knowledge misses important aspects

Handling:

• Break question into components
• Generate knowledge for each component
• Synthesize knowledge before answering
• Use multiple focused knowledge requests

Questions Requiring Recent Information:

Problem: Knowledge cutoff prevents accurate answers

Detection: Topics after training date, rapidly changing information

Handling:

• Detect time-sensitive queries
• Fall back to retrieval
• Acknowledge limitations
• Focus knowledge on stable background

Graceful Degradation:

def robust_gkp(question):
    """GKP with fallback mechanisms."""

    try:
        # Attempt GKP
        knowledge = generate_knowledge(question)

        # Quality check
        if is_knowledge_relevant(knowledge, question):
            return answer_with_knowledge(question, knowledge)
        else:
            # Fall back to direct prompting
            return direct_answer(question)

    except Exception as e:
        # Error fallback
        return direct_answer(question)

Constraint Management

Balancing Knowledge Quantity vs Quality:

• More knowledge provides more context but increases noise
• Approach: Start with 3-5 facts, adjust based on task
• Filter for relevance before integration
• Quality over quantity

Accuracy vs Latency:

• Higher accuracy needs more samples (more latency)
• Single-sample: Fast, moderate improvement
• Ensemble: Slower, better improvement
• Choose based on application requirements

Reliability vs Flexibility:

• Self-generated knowledge: Flexible but may hallucinate
• Retrieved knowledge: Reliable but requires infrastructure
• Hybrid: Use GKP with retrieval verification for critical applications

Context Window Constraints:

When knowledge + question + examples exceed context:

• Reduce few-shot examples
• Generate more concise knowledge
• Prioritize most relevant knowledge
• Split into multiple calls if necessary

Handling Incomplete Information:

When generated knowledge is insufficient:

• Request additional knowledge generation
• Acknowledge knowledge gaps in answer
• Combine with retrieval for missing information
• Generate from multiple perspectives

Error Handling:

def handle_gkp_errors(question):
    """Error handling for GKP pipeline."""

    # Knowledge generation failure
    try:
        knowledge = generate_knowledge(question)
    except APIError:
        return fallback_direct_answer(question)

    # Empty or low-quality knowledge
    if not knowledge or len(knowledge) < 50:
        return fallback_direct_answer(question)

    # Answer generation failure
    try:
        answer = answer_with_knowledge(question, knowledge)
    except APIError:
        # Try direct answer with cached knowledge context
        return answer_without_explicit_integration(question)

    return answer

Advanced Techniques

Clarity and Context Optimization

Ensuring Clarity in Knowledge Generation:

• Use specific, concrete instructions
• Request factual statements (not opinions)
• Specify knowledge format (bullets, sentences)
• Include format examples
• Request relevant, not comprehensive, knowledge

Removing Ambiguity:

• Define terms in knowledge request
• Specify the domain or context
• Request knowledge from specific perspectives
• Include disambiguation in few-shot examples

Example of Clear Knowledge Request:

Generate 3-4 specific, factual statements that would help answer this question about [domain/topic].

Focus on: [specific aspects relevant to the question]
Format: Brief factual statements
Do not include: opinions, speculation, or overly general information

Question: [question]

Knowledge:

Context Optimization:

• Include only relevant examples
• Keep examples concise but complete
• Match example complexity to task complexity
• Remove redundant information

Handling Context Length Limitations:

• Prioritize most relevant examples
• Compress knowledge to essential facts
• Use shorter example questions
• Split complex queries into sub-queries

Example Design:

Effective few-shot examples have:

• Clear question-knowledge mapping
• Diverse topics and knowledge types
• Appropriate length (not too long, not too short)
• Factually accurate information
• Format consistency

Advanced Knowledge Generation Patterns

Multi-Perspective Knowledge:

Generate knowledge from multiple perspectives that would help answer this question.

Question: Is nuclear energy safe?

Scientific perspective:
[Facts about nuclear physics, safety systems]

Historical perspective:
[Facts about nuclear incidents, safety record]

Environmental perspective:
[Facts about environmental impact, comparisons]

Economic perspective:
[Facts about costs, efficiency]

Hierarchical Knowledge:

Generate knowledge at different levels of specificity.

Question: How do vaccines work?

General:
[Broad overview of vaccination principle]

Specific:
[Detailed mechanism of immune response]

Technical:
[Scientific details for expert understanding]

Contrastive Knowledge:

Generate knowledge that helps distinguish between similar concepts.

Question: What's the difference between viruses and bacteria?

Viruses:
[Key characteristics of viruses]

Bacteria:
[Key characteristics of bacteria]

Key differences:
[Distinguishing features]

Conditional Knowledge:

Generate knowledge that addresses different scenarios.

Question: Should I invest in stocks?

If risk-tolerant and long time horizon:
[Relevant knowledge for this scenario]

If risk-averse or short time horizon:
[Relevant knowledge for this scenario]

General considerations:
[Universally relevant knowledge]

Self-Verification and Quality Control

Knowledge Verification Step:

def verified_gkp(question):
    """GKP with knowledge verification."""

    # Generate knowledge
    knowledge = generate_knowledge(question)

    # Verify knowledge
    verification_prompt = f"""
    Review the following knowledge for accuracy and relevance.

    Question: {question}

    Generated Knowledge:
    {knowledge}

    For each fact:
    1. Is it factually accurate? (Yes/No/Uncertain)
    2. Is it relevant to the question? (Yes/No)

    Verified Knowledge (include only accurate and relevant facts):
    """

    verified_knowledge = llm(verification_prompt)

    # Answer with verified knowledge
    return answer_with_knowledge(question, verified_knowledge)

Uncertainty Quantification:

Generate knowledge for this question. For each fact, indicate your confidence level.

Question: [question]

Knowledge:
1. [Fact] - Confidence: [High/Medium/Low]
2. [Fact] - Confidence: [High/Medium/Low]
...

Self-Consistency Check:

def consistency_checked_gkp(question, n_samples=3):
    """Generate multiple knowledge samples and check consistency."""

    knowledge_samples = []
    for _ in range(n_samples):
        knowledge = generate_knowledge(question, temperature=0.7)
        knowledge_samples.append(knowledge)

    # Check consistency
    consistency_prompt = f"""
    Review these {n_samples} knowledge generations for the same question.
    Identify facts that appear consistently across generations.

    Question: {question}

    Knowledge Set 1: {knowledge_samples[0]}
    Knowledge Set 2: {knowledge_samples[1]}
    Knowledge Set 3: {knowledge_samples[2]}

    Consistent Facts (appear in 2+ sets):
    """

    consistent_knowledge = llm(consistency_prompt)

    return answer_with_knowledge(question, consistent_knowledge)

Structured Output Control

JSON-Formatted Knowledge:

def structured_gkp(question):
    """Generate structured JSON knowledge."""

    knowledge_prompt = f"""
    Generate knowledge as a JSON object.

    Question: {question}

    Format:
    {{
        "main_facts": ["fact1", "fact2", "fact3"],
        "definitions": {{"term1": "definition1"}},
        "relationships": ["A relates to B because...", ...],
        "confidence": "high/medium/low"
    }}

    Knowledge JSON:
    """

    knowledge_json = llm(knowledge_prompt)
    knowledge = json.loads(knowledge_json)

    # Format for integration
    formatted_knowledge = format_knowledge(knowledge)

    return answer_with_knowledge(question, formatted_knowledge)

Categorized Knowledge:

Generate knowledge organized by category.

Question: [question]

Definitions:
- [Term]: [Definition]

Facts:
- [Fact 1]
- [Fact 2]

Relationships:
- [How concepts relate]

Context:
- [Background information]

Interaction Patterns

Conversational GKP:

For multi-turn conversations, maintain knowledge context:

class ConversationalGKP:
    def __init__(self):
        self.accumulated_knowledge = []

    def ask(self, question):
        # Generate new knowledge
        new_knowledge = generate_knowledge(question)

        # Add to accumulated knowledge
        self.accumulated_knowledge.append({
            "question": question,
            "knowledge": new_knowledge
        })

        # Answer with accumulated knowledge
        all_knowledge = self.format_accumulated_knowledge()
        return answer_with_knowledge(question, all_knowledge)

    def format_accumulated_knowledge(self):
        """Format accumulated knowledge for context."""
        if len(self.accumulated_knowledge) > 3:
            # Keep only recent knowledge to manage context
            recent = self.accumulated_knowledge[-3:]
        else:
            recent = self.accumulated_knowledge

        return "\n\n".join([
            f"[For: {item['question']}]\n{item['knowledge']}"
            for item in recent
        ])

Iterative Refinement:

def iterative_gkp(question, max_iterations=3):
    """Iteratively refine knowledge and answers."""

    knowledge = generate_knowledge(question)
    answer = answer_with_knowledge(question, knowledge)

    for i in range(max_iterations - 1):
        # Check if answer is satisfactory
        evaluation = evaluate_answer_quality(question, answer)

        if evaluation["satisfactory"]:
            break

        # Generate additional knowledge addressing gaps
        refinement_prompt = f"""
        The current answer may be incomplete or incorrect.

        Question: {question}
        Current Knowledge: {knowledge}
        Current Answer: {answer}
        Issues: {evaluation["issues"]}

        Generate additional knowledge to address these issues:
        """

        additional_knowledge = llm(refinement_prompt)
        knowledge = knowledge + "\n\n" + additional_knowledge
        answer = answer_with_knowledge(question, knowledge)

    return answer

Chained Knowledge Generation:

For complex questions requiring knowledge from multiple domains:

def chained_gkp(question):
    """Chain knowledge generation across domains."""

    # Identify required knowledge domains
    domain_prompt = f"""
    What domains of knowledge are needed to answer this question?

    Question: {question}

    Domains (list 2-4):
    """

    domains = extract_domains(llm(domain_prompt))

    # Generate knowledge for each domain
    all_knowledge = []
    for domain in domains:
        domain_knowledge = generate_knowledge(
            f"[Domain: {domain}] {question}"
        )
        all_knowledge.append(f"[{domain}]\n{domain_knowledge}")

    combined_knowledge = "\n\n".join(all_knowledge)

    return answer_with_knowledge(question, combined_knowledge)

Model Considerations

Cross-Model Behavior:

GPT-4:

• Generates well-structured knowledge
• Good at following format instructions
• May include caveats and qualifications
• Strong factual accuracy for common knowledge

Claude:

• Conversational knowledge style
• Good at nuanced, balanced knowledge
• May be more cautious about uncertain facts
• Excellent at distinguishing fact from opinion

Gemini:

• Good at structured formats
• Strong multimodal knowledge (if applicable)
• May provide more detailed knowledge
• Good for technical domains

Open-source (Llama, Mistral):

• Variable quality depending on model size
• May need more explicit instructions
• Simpler knowledge format works better
• 70B+ parameters recommended

Adapting for Model Capabilities:

def adaptive_gkp(question, model_name):
    """Adapt GKP approach based on model."""

    if "gpt-4" in model_name:
        # GPT-4: Can handle complex instructions
        return standard_gkp(question)

    elif "claude" in model_name:
        # Claude: Benefits from conversational framing
        return conversational_gkp(question)

    elif "llama" in model_name or "mistral" in model_name:
        # Open-source: Simpler instructions, more examples
        return simplified_gkp(question, num_examples=5)

    else:
        # Default: Conservative approach
        return single_prompt_gkp(question)

Handling Model Updates:

• Re-test GKP prompts with new model versions
• Knowledge quality may change
• Adjust few-shot examples if needed
• Monitor production performance after updates

Cross-Model Portability:

For prompts that work across models:

• Use simple, explicit instructions
• Avoid model-specific syntax
• Include more examples for robustness
• Test on target models before deployment

Evaluation and Efficiency

Measuring GKP Effectiveness:

def comprehensive_evaluation(test_set):
    """Evaluate GKP across multiple dimensions."""

    results = {
        "accuracy": [],
        "knowledge_accuracy": [],
        "knowledge_relevance": [],
        "latency": [],
        "token_usage": []
    }

    for question, expected in test_set:
        start_time = time.time()

        # Generate knowledge
        knowledge = generate_knowledge(question)

        # Evaluate knowledge quality (human or automated)
        k_accuracy = evaluate_factual_accuracy(knowledge)
        k_relevance = evaluate_relevance(knowledge, question)

        # Generate answer
        answer = answer_with_knowledge(question, knowledge)

        # Evaluate answer
        correct = evaluate_answer(answer, expected)

        # Metrics
        latency = time.time() - start_time
        tokens = count_tokens(knowledge) + count_tokens(answer)

        results["accuracy"].append(correct)
        results["knowledge_accuracy"].append(k_accuracy)
        results["knowledge_relevance"].append(k_relevance)
        results["latency"].append(latency)
        results["token_usage"].append(tokens)

    return {
        "accuracy": np.mean(results["accuracy"]),
        "knowledge_accuracy": np.mean(results["knowledge_accuracy"]),
        "knowledge_relevance": np.mean(results["knowledge_relevance"]),
        "avg_latency": np.mean(results["latency"]),
        "avg_tokens": np.mean(results["token_usage"])
    }

Token Optimization:

def optimized_gkp(question):
    """Token-optimized GKP implementation."""

    # Concise knowledge request
    knowledge_prompt = f"""Facts for: {question}

1.
2.
3."""

    knowledge = llm(knowledge_prompt, max_tokens=150)

    # Minimal integration
    answer_prompt = f"""K: {knowledge}
Q: {question}
A:"""

    return llm(answer_prompt, max_tokens=100)

Batching for Efficiency:

async def batch_gkp(questions):
    """Process multiple questions in parallel."""

    # Generate all knowledge in parallel
    knowledge_tasks = [
        generate_knowledge_async(q) for q in questions
    ]
    knowledge_list = await asyncio.gather(*knowledge_tasks)

    # Generate all answers in parallel
    answer_tasks = [
        answer_with_knowledge_async(q, k)
        for q, k in zip(questions, knowledge_list)
    ]
    answers = await asyncio.gather(*answer_tasks)

    return answers

Safety, Robustness, and Domain Adaptation

Preventing Hallucination Propagation:

def safe_gkp(question):
    """GKP with hallucination safeguards."""

    # Generate knowledge with uncertainty markers
    knowledge_prompt = f"""
    Generate factual knowledge for this question.
    Mark uncertain facts with [UNCERTAIN].
    Only include facts you are confident about.

    Question: {question}

    Knowledge:
    """

    knowledge = llm(knowledge_prompt)

    # Filter uncertain facts
    filtered_knowledge = filter_uncertain(knowledge)

    # Answer with filtered knowledge
    return answer_with_knowledge(question, filtered_knowledge)

Input Validation:

def validated_gkp(question):
    """GKP with input validation."""

    # Check for injection attempts
    if contains_injection_patterns(question):
        return "I cannot process this request."

    # Check for appropriate question type
    if not benefits_from_knowledge(question):
        return direct_answer(question)

    return standard_gkp(question)

Domain Adaptation:

def domain_adapted_gkp(question, domain):
    """GKP adapted for specific domain."""

    # Domain-specific knowledge request
    domain_prompts = {
        "medical": "Generate medical knowledge (educational only, not medical advice):",
        "legal": "Generate legal concepts (educational only, not legal advice):",
        "technical": "Generate technical knowledge:",
        "general": "Generate relevant knowledge:"
    }

    prompt = domain_prompts.get(domain, domain_prompts["general"])

    # Domain-specific examples
    examples = load_domain_examples(domain)

    full_prompt = f"""
    {format_examples(examples)}

    {prompt}
    Question: {question}

    Knowledge:
    """

    knowledge = llm(full_prompt)

    return answer_with_knowledge(question, knowledge)

Quick Domain Adaptation:

For new domains with limited examples:

1. Create 3-5 domain-specific knowledge examples
2. Include domain terminology in instructions
3. Test on domain-specific questions
4. Iterate based on failure analysis
5. Consider domain experts for validation

Risk and Ethics

Ethical Considerations

Misinformation Risk:

GKP generates knowledge from model parameters, which may contain errors, biases, or outdated information. Unlike retrieval from verified sources, generated knowledge has no external validation.

Implications:

• Plausible-sounding but incorrect facts may be presented as truth
• Users may trust generated knowledge inappropriately
• Errors propagate through the answer with high confidence
• Particularly risky for factual, medical, legal, or financial information

Mitigation:

• Clearly communicate that knowledge is AI-generated
• Verify critical facts through external sources
• Include appropriate disclaimers
• Use retrieval for high-stakes applications
• Implement fact-checking mechanisms

Bias Amplification:

Generated knowledge may reflect biases in training data:

• Cultural and geographic biases
• Temporal biases (reflecting historical perspectives)
• Demographic biases in examples and representations
• Domain biases (overrepresentation of certain fields)

Mitigation:

• Audit generated knowledge for bias
• Use diverse evaluation sets
• Include counter-examples in few-shot prompts
• Monitor for problematic patterns
• Consider debiasing techniques

Transparency Concerns:

Users may not understand:

• That knowledge is generated, not retrieved
• Limitations of model's knowledge
• Potential for hallucination
• Difference from verified sources

Recommendations:

• Label AI-generated knowledge clearly
• Explain the GKP process when relevant
• Provide confidence indicators
• Acknowledge limitations

Capability Concerns:

GKP demonstrates that models can leverage their own knowledge to improve performance. This has implications for:

• Self-improvement potential
• Autonomous knowledge synthesis
• Reduced dependence on external verification

Risk Analysis

Failure Modes:

1. Hallucinated Knowledge → Wrong Answer:

Question: Who won the 2025 World Series?
Generated Knowledge: The Texas Rangers won the 2025 World Series... [hallucination]
Answer: Texas Rangers [confidently wrong]

Detection: Verify against external sources, check knowledge consistency Mitigation: Use retrieval for factual queries, acknowledge uncertainty

2. Irrelevant Knowledge → No Improvement:

Question: What is 15 × 7?
Generated Knowledge: Mathematics is the study of numbers... [irrelevant]
Answer: [No improvement over baseline]

Detection: Measure GKP vs baseline performance Mitigation: Detect and skip GKP for non-beneficial queries

3. Biased Knowledge → Biased Answer:

Question: Who makes better leaders?
Generated Knowledge: [Reflects biases in training data]
Answer: [Propagates bias]

Detection: Bias auditing, diverse evaluation Mitigation: Balanced few-shot examples, bias filtering

Cascading Failures:

Single hallucinated fact can:

• Become premise for flawed reasoning
• Be combined with correct facts to create plausible but wrong synthesis
• Be presented with high confidence
• Influence subsequent questions in conversation

Safety Concerns:

Medical/Legal/Financial Domains:

GKP should not replace professional advice. Generated knowledge may be:

• Outdated
• Incomplete
• Misapplied to specific situations
• Wrong

Recommendations:

• Include prominent disclaimers
• Use GKP for educational context only
• Require human verification for actionable advice
• Consider domain-specific safeguards

Adversarial Risks:

• Prompt injection through questions
• Eliciting harmful knowledge
• Manipulating knowledge generation

Mitigation:

• Input validation
• Output filtering
• Content safety checks
• Rate limiting

Innovation Potential

Derived Innovations:

1. Self-Improving Knowledge:

Models can generate, verify, and refine their own knowledge, potentially leading to:

• Automated knowledge base construction
• Self-correcting information systems
• Iterative knowledge refinement

2. Hybrid Knowledge Systems:

Combining GKP with retrieval for:

• Generated knowledge verified against retrieved sources
• Retrieved facts supplemented with inferred knowledge
• Dynamic knowledge selection based on availability

3. Compositional Knowledge:

Breaking knowledge into components for:

• Modular knowledge generation
• Cross-domain knowledge synthesis
• Knowledge reuse across queries

Novel Combinations:

GKP + Chain-of-Thought:

Generate knowledge first, then reason through it:

Step 1: Generate relevant knowledge
Step 2: Reason through the knowledge step-by-step
Step 3: Arrive at answer

GKP + Self-Consistency:

Generate multiple knowledge sets, reason through each, vote on answers.

GKP + Verification:

Generate knowledge, verify against external sources, use only verified knowledge.

GKP + Active Learning:

Identify knowledge gaps, request human input for uncertain areas.

Ecosystem and Integration

Tools and Frameworks

LangChain:

• Prompt templates for knowledge generation
• Chain composition for two-stage pipeline
• Output parsing for structured knowledge
• Integration with various LLMs

DSPy:

• Signature-based knowledge prompts
• Automated optimization of few-shot examples
• Evaluation and testing frameworks
• Modular GKP implementation

LlamaIndex:

• Knowledge integration with document stores
• Hybrid GKP + retrieval pipelines
• Structured knowledge handling

Pre-built Resources:

• Prompt Engineering Guide: GKP examples and tutorials
• Learn Prompting: Interactive GKP demonstrations
• Original paper code: github.com/liujch1998/GKP
• Community implementations and variations

Evaluation Tools:

• Custom accuracy calculators
• Knowledge quality assessment frameworks
• A/B testing infrastructure
• Human evaluation interfaces

Related Techniques and Comparisons

Closely Related:

Retrieval-Augmented Generation (RAG):

• GKP: Generates knowledge from model parameters
• RAG: Retrieves knowledge from external documents
• GKP: No external infrastructure needed
• RAG: More reliable for factual information

| Aspect | GKP | RAG | | ---------------- | -------------------------------- | -------------------------- | | Knowledge source | Model parameters | External documents | | Infrastructure | None | Vector DB, embeddings | | Reliability | Variable (may hallucinate) | Higher (verified sources) | | Recency | Limited by training cutoff | Up-to-date | | Flexibility | Works for any domain model knows | Limited to indexed content | | Cost | 2x LLM calls | Retrieval + LLM |

Chain-of-Thought (CoT):

• GKP: Generates knowledge (facts, context)
• CoT: Generates reasoning (logic, steps)
• GKP: For knowledge-dependent tasks
• CoT: For reasoning-dependent tasks

Self-Ask:

• Related approach generating intermediate questions
• More structured than GKP
• Better for multi-hop reasoning
• GKP better for factual grounding

Analogical Prompting:

• Extension of GKP concept
• Generates relevant examples and analogies
• Builds on knowledge generation principles

Hybrid Solutions:

GKP + RAG:

def hybrid_knowledge(question):
    """Combine generated and retrieved knowledge."""

    # Generate knowledge from model
    generated = generate_knowledge(question)

    # Retrieve knowledge from documents
    retrieved = retrieve_documents(question)

    # Combine and deduplicate
    combined = f"""
    Generated Knowledge:
    {generated}

    Retrieved Information:
    {retrieved}
    """

    return answer_with_knowledge(question, combined)

GKP + CoT:

def knowledge_enhanced_reasoning(question):
    """Knowledge generation followed by reasoning."""

    # Stage 1: Generate relevant knowledge
    knowledge = generate_knowledge(question)

    # Stage 2: Reason through with knowledge
    reasoning_prompt = f"""
    Use this knowledge to reason through the question step by step.

    Knowledge: {knowledge}

    Question: {question}

    Let's think step by step:
    """

    return llm(reasoning_prompt)

Integration Patterns

Task Adaptation:

Question Answering:

• Generate knowledge about entities/concepts in question
• Include definitional and relational knowledge
• Use multiple knowledge samples for complex questions

Classification:

• Generate knowledge about class characteristics
• Include distinguishing features
• Request contrastive knowledge

Text Generation:

• Generate background knowledge about topic
• Include relevant facts and context
• Request domain-specific information

Integration with RAG:

Pattern 1: GKP First, RAG Fallback

def gkp_with_rag_fallback(question):
    """Use GKP, fall back to RAG if knowledge seems unreliable."""

    knowledge = generate_knowledge(question)

    # Check knowledge quality
    if is_knowledge_reliable(knowledge):
        return answer_with_knowledge(question, knowledge)
    else:
        # Fall back to retrieval
        retrieved = retrieve_documents(question)
        return answer_with_knowledge(question, retrieved)

Pattern 2: Parallel Generation

def parallel_knowledge(question):
    """Generate and retrieve in parallel, combine best."""

    # Parallel execution
    generated = generate_knowledge(question)
    retrieved = retrieve_documents(question)

    # Select or combine based on quality/relevance
    knowledge = select_best_knowledge(generated, retrieved, question)

    return answer_with_knowledge(question, knowledge)

Integration with Agents:

class KnowledgeAugmentedAgent:
    """Agent that uses GKP for knowledge-intensive tasks."""

    def decide_action(self, state, query):
        # Generate knowledge about the situation
        knowledge = generate_knowledge(f"Context: {state}\nQuery: {query}")

        # Decide action based on knowledge
        action_prompt = f"""
        Knowledge: {knowledge}
        Current State: {state}
        Query: {query}

        What action should be taken?
        """

        return self.llm(action_prompt)

Transition Strategies:

From Direct Prompting to GKP:

1. Identify tasks where direct prompting fails on knowledge-dependent questions
2. Test GKP on subset of problematic queries
3. Measure accuracy improvement
4. Gradually expand GKP to beneficial use cases
5. Maintain direct prompting for simple queries

From GKP to RAG:

1. Identify queries where generated knowledge is unreliable
2. Build retrieval infrastructure for critical domains
3. Implement hybrid approach
4. Transition high-stakes queries to retrieval
5. Keep GKP for general queries where it performs well

Production Integration:

class ProductionGKP:
    """Production-ready GKP implementation."""

    def __init__(self, config):
        self.config = config
        self.cache = KnowledgeCache()
        self.monitor = QualityMonitor()

    def answer(self, question):
        # Check cache
        if cached := self.cache.get(question):
            return cached

        # Generate knowledge
        knowledge = self.generate_knowledge(question)

        # Quality check
        quality = self.monitor.assess(knowledge)
        if quality < self.config.min_quality:
            return self.fallback(question)

        # Generate answer
        answer = self.answer_with_knowledge(question, knowledge)

        # Cache and log
        self.cache.set(question, answer)
        self.monitor.log(question, knowledge, answer, quality)

        return answer

    def fallback(self, question):
        """Fallback for low-quality knowledge."""
        if self.config.rag_enabled:
            return self.rag_answer(question)
        else:
            return self.direct_answer(question)

Future Directions

Emerging Innovations

Knowledge Verification Integration:

Combining GKP with automated fact-checking:

• Generate knowledge
• Verify against trusted sources
• Filter or correct hallucinations
• Present verified knowledge for answering

Adaptive Knowledge Generation:

Systems that adapt knowledge generation based on:

• Question complexity
• Domain requirements
• Available context
• User expertise level

Multi-Modal Knowledge:

Extending GKP to generate knowledge from:

• Images (visual knowledge generation)
• Tables and structured data
• Code and technical artifacts
• Multi-document synthesis

Personalized Knowledge:

Adapting knowledge generation to:

• User's knowledge level
• Previous conversation context
• Domain expertise
• Specific information needs

Knowledge Graph Integration:

Combining GKP with structured knowledge:

• Generate knowledge as graph triples
• Integrate with existing knowledge graphs
• Enable structured reasoning over generated knowledge

Research Frontiers

Faithfulness of Generated Knowledge:

• How accurate is self-generated knowledge?
• Can we improve factual accuracy without external verification?
• What makes some knowledge generations more reliable?
• How does model size affect knowledge quality?

Optimal Knowledge Generation Strategies:

• What types of knowledge are most helpful?
• How much knowledge is optimal for different tasks?
• When does more knowledge hurt performance?
• How to balance breadth vs depth of knowledge?

Cross-Domain Transfer:

• Can knowledge generation patterns transfer across domains?
• How to quickly adapt to new domains?
• What domain-general principles exist?
• How to leverage analogies across domains?

Efficiency Optimization:

• Can we generate effective knowledge with fewer tokens?
• How to identify when GKP is beneficial vs wasteful?
• Adaptive approaches that skip GKP when unnecessary
• Compressed knowledge representations

Reliability and Verification:

• Automated hallucination detection in generated knowledge
• Self-consistency methods for knowledge verification
• Confidence calibration for generated facts
• Integration with external verification systems

Theoretical Understanding:

• Why does self-generated knowledge help?
• What properties of knowledge are most useful?
• How does knowledge interact with model reasoning?
• Formal models of knowledge-enhanced inference

Human-AI Collaboration:

• Human-in-the-loop knowledge verification
• Interactive knowledge refinement
• Expertise integration with generated knowledge
• Explanation and transparency of knowledge sources

The future of Generated Knowledge Prompting points toward:

1. Hybrid systems combining generation with retrieval for reliability
2. Verification mechanisms ensuring knowledge accuracy
3. Adaptive approaches that apply GKP when beneficial
4. Multi-modal extensions beyond text knowledge
5. Theoretical foundations explaining why and when GKP works
6. Safer implementations with better hallucination handling

Generated Knowledge Prompting represents a fundamental insight: language models contain more knowledge than they typically express during direct prompting. By explicitly requesting this knowledge, we can improve performance on knowledge-dependent tasks without external infrastructure. As models grow more capable and verification methods improve, GKP will evolve from a prompting technique to an integrated capability, seamlessly surfacing relevant knowledge when needed for improved reasoning and accuracy.