What is Generate Knowledge Prompting?

Generate Knowledge Prompting is a two-stage technique: first, ask the model to generate relevant facts and background knowledge about the question. Second, use that generated knowledge as context when asking the model to answer. By retrieving its own knowledge before answering, the model is less likely to miss important background information that changes the correct answer.

How is Generate Knowledge different from Chain of Thought?

Chain of Thought shows the reasoning process — how to get from question to answer. Generate Knowledge focuses on retrieving relevant facts and world knowledge before reasoning begins. You can combine both: generate knowledge, then reason through it with CoT. Generate Knowledge works especially well for commonsense questions where background context matters.

When should I use Generate Knowledge Prompting?

Use it for questions requiring commonsense knowledge, domain expertise, or historical/factual context that the model might not surface on its own. It's particularly effective for: medical/legal/scientific questions, culturally specific questions, anything where background context significantly changes the correct answer, and questions that sound simple but require nuanced domain knowledge.

Can I use externally retrieved knowledge instead of generated knowledge?

Yes — combining Generate Knowledge with RAG is a powerful pattern. Use RAG to retrieve external documents, then have the model also generate any additional relevant knowledge not in those documents, and finally answer using both sources. This is sometimes called 'knowledge-augmented generation' and addresses both the freshness problem (RAG handles) and the implicit knowledge problem (generate knowledge handles).

Generate Knowledge Prompting: Let the Model Teach Itself Before Answering

Generate Knowledge Prompting is a two-step technique that dramatically improves accuracy by asking the model to surface relevant facts before attempting to answer.

The Core Problem

LLMs store enormous amounts of knowledge, but they don't always retrieve the right knowledge for a given question. They answer from whatever comes to mind first — which may not include the background context that would change the answer.

Generate Knowledge Prompting fixes this by making the knowledge retrieval step explicit.

The Two-Step Pattern

Step 1: Generate Knowledge

Ask the model to produce relevant facts, background context, or domain knowledge about the topic:

Generate a list of key facts and background knowledge relevant to this question:

Question: [Your question here]

Write 3-5 factual statements that would help someone answer this question accurately.

Step 2: Answer Using Generated Knowledge

Pass the generated knowledge as context and ask for the final answer:

Using the following background knowledge, answer the question:

Knowledge:
[Paste or inject the knowledge from Step 1]

Question: [Your original question]

Answer:

Side-by-Side Example

Without Generate Knowledge:

Q: If you plant tomatoes near basil, what happens?

A: They grow well together. Basil repels some insects.

(Accurate but shallow — misses why this works and its limitations)

With Generate Knowledge:

Step 1 — Generate knowledge:
"Generate background knowledge about companion planting with tomatoes and basil."

Generated knowledge:
- Basil produces volatile compounds (linalool, eugenol) that repel thrips, aphids,
  and certain moths
- Some studies show tomatoes planted near basil have improved flavor, though
  evidence is mixed
- Basil prefers similar growing conditions to tomatoes: warm weather, full sun
- Basil flowers compete with tomatoes for pollinators if not pinched back
- Root competition is minimal — they have different root depths

Step 2 — Answer:
"Using this background knowledge, what happens if you plant tomatoes near basil?"

A: Planting basil near tomatoes has several benefits: basil's volatile compounds
(linalool, eugenol) repel thrips and aphids, potentially reducing pest damage.
Both plants thrive in similar conditions. However, you should pinch basil flowers
to prevent competition for pollinators, and the flavor improvement often cited
has mixed scientific support. Overall, it's a beneficial pairing with some
maintenance considerations.

When to Use It

Generate Knowledge is most effective when:

Scenario	Why it helps
Commonsense questions	Surfaces implicit knowledge the model might skip
Medical / legal / scientific topics	Forces the model to recall domain specifics before opining
Cultural / historical context questions	Retrieves relevant background the model might not prioritize
Questions with counterintuitive answers	Grounds the model in facts before it jumps to an obvious-but-wrong answer
"What would happen if..." scenarios	Establishes baseline facts before projecting outcomes

Less useful for:

Simple factual lookups ("What year was X founded?")
Creative generation tasks
Tasks where the model is already highly accurate

Single-Prompt Version

You can do both steps in one prompt using a clear structure:

Question: [Your question]

Before answering, write out 3-5 key facts or pieces of background knowledge
relevant to this question. Then, using those facts, provide your final answer.

Format your response as:
KNOWLEDGE:
- [fact 1]
- [fact 2]
- [fact 3]

ANSWER:
[Your answer, grounded in the above facts]

This is simpler to implement and works well for straightforward use cases.

Chaining with Other Techniques

Generate Knowledge combines well with other prompting techniques:

Generate Knowledge + Chain of Thought:

Step 1: Generate relevant knowledge
Step 2: Use that knowledge + CoT reasoning to solve the problem

Generate Knowledge + Few-Shot:

Step 1: Generate knowledge about the problem type
Step 2: Provide examples (few-shot) of solving similar problems
Step 3: Solve the actual problem

Generate Knowledge + RAG:

Step 1: Retrieve external documents (RAG)
Step 2: Generate additional in-model knowledge not in documents
Step 3: Answer using both retrieved and generated knowledge

Limitations

Adds latency and cost: Two LLM calls instead of one (unless single-prompt version)
Generated knowledge may contain errors: The model can generate plausible-sounding but wrong facts — especially in highly specialized domains
Less useful with strong CoT: If chain-of-thought already captures the relevant reasoning, Generate Knowledge adds little

Mitigation: Use external retrieval (RAG) for factual accuracy when possible, and treat generated knowledge as a reasoning scaffold rather than a source of truth.

Key Takeaways

Ask the model to generate relevant background knowledge before answering
Use that knowledge as context for the final answer
Works best for commonsense, scientific, and domain-expert questions
Can be done in one prompt or two separate calls
Combine with RAG to handle both "what the model knows" and "what the documents say"