Generated Knowledge Prompting
Before asking the model to answer, ask it to teach itself first. Generated Knowledge Prompting uses a two-step process — generate relevant knowledge, then reason with it — turning the model’s own latent understanding into explicit context that improves accuracy by 14–20%.
Introduced: Generated Knowledge Prompting was published in 2022 by Liu et al. The technique takes a two-step approach: first, prompt the model to generate relevant factual knowledge about the topic; then, include that generated knowledge in a second prompt alongside the actual question. The original paper demonstrated improvements of 14–20% on commonsense reasoning benchmarks (NumerSense, CSQA2) by having the model “prime itself” with its own knowledge before answering. The key insight was that LLMs contain vast knowledge but don’t always surface the right facts when answering directly.
Modern LLM Status: Generated Knowledge Prompting was a precursor to Retrieval-Augmented Generation (RAG). While RAG now retrieves real documents from external knowledge bases, the core insight — priming context before answering — remains powerful. In 2026, the technique is most useful when external retrieval isn’t available but the model’s own knowledge can be surfaced and organized before reasoning. Modern models with extended thinking capabilities (like Claude’s) partially internalize this pattern, but explicit knowledge generation still helps for specialized or nuanced topics where the model benefits from “warming up” its relevant knowledge before committing to an answer.
Let the Model Study Before the Exam
When you ask a question directly, the model attempts to retrieve the right facts and reason with them simultaneously. This combined cognitive load often leads to shallow or inaccurate answers — the model grabs whatever knowledge surfaces first rather than the most relevant knowledge.
Generated Knowledge separates retrieval from reasoning. Step one: ask the model to generate everything it knows about the topic. Step two: feed that generated knowledge back as context for the actual question. By splitting the process, you give the model a chance to surface relevant facts deliberately, then reason over them with full context in view.
Think of it like asking a student to write down everything they know about photosynthesis on a notepad before answering an exam question about it. The act of writing primes the right knowledge, and having it visible during the answer prevents gaps and errors.
LLMs contain far more knowledge than they surface in any single response. When answering directly, the model’s attention mechanism selects a subset of relevant information — but this selection is noisy and can miss crucial facts. By explicitly asking the model to generate knowledge first, you force a broader retrieval pass. The generated knowledge then sits in the context window during the actual answer, ensuring the model has access to its own best information. This is especially powerful for commonsense reasoning, where the relevant facts feel “obvious” but are easy to overlook.
The Generated Knowledge Process
Two stages: generate knowledge, then reason with it
Identify the Question Domain
Start with the question you need answered. Identify the key topic, domain, or concept area that the model will need knowledge about. This helps you craft an effective knowledge generation prompt.
Question: “Is it possible for a penguin to fly higher than a typical commercial airplane?” — Domain: penguin biology, avian flight capabilities, airplane altitude.
Generate Relevant Knowledge
Prompt the model to generate factual knowledge about the topic without yet asking the question. The goal is to surface relevant facts, context, and background information that will be useful for answering. Generate multiple knowledge statements for broader coverage.
“Generate 5 facts about penguins and their physical abilities, including their relationship to flight.” — The model produces: Penguins are flightless birds. Their wings evolved into flippers for swimming. Emperor penguins can dive to 1,800 feet. No penguin species can fly. Commercial airplanes cruise at 35,000–42,000 feet.
Combine Knowledge with the Question
Create a second prompt that includes the generated knowledge as context, followed by the actual question. The model now has explicit access to relevant facts while formulating its answer, leading to more informed and accurate reasoning.
“Given the following knowledge: [generated facts about penguins and flight]. Now answer: Is it possible for a penguin to fly higher than a typical commercial airplane?”
Verify the Answer Against the Knowledge
Review the model’s answer to ensure it correctly uses the generated knowledge and doesn’t contradict it. Check for logical consistency between the knowledge statements and the conclusion. Remember that the generated knowledge itself may contain errors — always verify important claims against trusted sources.
“No, penguins cannot fly at all. They are flightless birds whose wings evolved into flippers. Since they cannot achieve any altitude through flight, they cannot fly higher than a commercial airplane (or anything else). Note: The generated knowledge was consistent, but the user should verify these biological claims against a reliable reference.”
See the Difference
Why knowledge priming produces more accurate answers
Direct Answer
Part of golf is trying to get a lower point total than others. True or False?
True.
Generated Knowledge
Generated knowledge: Golf is scored by counting strokes. Each stroke adds one point. The player with the fewest total strokes (lowest score) wins. Par is the expected number of strokes for a hole. Scoring below par (birdie, eagle) is desirable. A higher score means worse performance.
True. In golf, each stroke counts as a point, and the objective is to complete the course in as few strokes as possible. The player with the lowest total score wins, making golf one of the few major sports where a lower score is better. Terms like “birdie” (one under par) and “eagle” (two under par) reflect this — scoring below the expected number is the goal.
Natural Language Works Too
While structured frameworks and contextual labels are powerful tools, LLMs are exceptionally good at understanding natural language. As long as your prompt contains the actual contextual information needed to create, answer, or deliver the response you’re looking for — the who, what, why, and constraints — the AI can produce complete and accurate results whether you use a formal framework or plain conversational language. But even in 2026, with the best prompts, verifying AI output is always a necessary step.
Generated Knowledge in Action
See how knowledge priming improves reasoning across domains
“Generate 5 relevant facts about glass, its properties, and how it interacts with sound and light.”
Generated: Glass is a solid but brittle material. Glass is transparent to visible light but can block UV rays. Glass can shatter when exposed to specific resonant frequencies of sound. Tempered glass is 4x stronger than regular glass. Sound travels through glass but is partially absorbed, making it useful for sound insulation.
Question: “Can a singer break a glass with their voice? Use the knowledge above to reason through your answer. Flag anything you are uncertain about.”
Answer: Yes, it is physically possible. Glass has a natural resonant frequency, and when a singer produces a sustained note at that exact frequency with sufficient amplitude (volume), the vibrations can exceed the glass’s structural tolerance and cause it to shatter. This has been demonstrated experimentally, though it requires exceptional vocal control and volume. Tempered glass would be much harder to break due to its 4x greater strength. Note: While the physics are well-established, verify specific claims about vocal amplitude thresholds against acoustics references.
“Generate relevant historical knowledge about the Silk Road, including its time period, routes, and cultural significance.”
Generated: The Silk Road was a network of trade routes active from roughly the 2nd century BCE to the 15th century CE. It connected East Asia to the Mediterranean through Central Asia. The routes carried silk, spices, precious metals, and other goods. The Silk Road facilitated the spread of religions including Buddhism, Islam, and Christianity. It was also a vector for disease transmission, including the Black Death. The Mongol Empire’s Pax Mongolica (13th–14th century) was a peak period of Silk Road activity.
Question: “Was the Silk Road more important for trade or cultural exchange? Use the knowledge above to support your reasoning. Acknowledge areas of historical debate.”
Answer: The two functions were inseparable, but the cultural impact arguably outlasted the commercial one. While trade in silk, spices, and metals drove the economic incentive to maintain the routes, the cultural exchanges they enabled — the spread of Buddhism from India to China, Islam across Central Asia, and scientific knowledge in both directions — reshaped civilizations permanently. Even when trade routes shifted to maritime paths in the 15th century, the religious and intellectual transformations they had catalyzed continued for centuries. Note: This is a matter of historical interpretation. Historians debate the relative weight of commercial vs. cultural significance. Verify specific claims against academic sources.
“Generate relevant technical knowledge about database indexing, including how indexes work, types of indexes, and their performance tradeoffs.”
Generated: Database indexes are data structures that speed up row retrieval at the cost of additional storage and write overhead. B-tree indexes are the most common and support range queries efficiently. Hash indexes are faster for exact-match queries but don’t support ranges. Composite indexes cover multiple columns but column order matters. Over-indexing slows write operations because every INSERT/UPDATE must maintain all indexes. Covering indexes include all queried columns, eliminating the need for table lookups.
Question: “My database query is slow. It filters on user_id and created_at but the table has 50 million rows. What indexing strategy should I consider? Use the knowledge above. Note any assumptions you are making.”
Answer: Create a composite B-tree index on (user_id, created_at) in that order. Since you’re filtering on user_id (likely equality) and created_at (likely a range), the B-tree index will first narrow to matching user_ids, then efficiently scan the date range within that subset. If you also SELECT specific columns frequently, consider a covering index that includes those columns to avoid table lookups entirely. However, if this is a write-heavy table, monitor the write performance impact of adding indexes. Assumptions: user_id is an equality filter, created_at is a range filter, and you’re using a relational database that supports B-tree indexes. Verify this strategy against your specific database engine’s documentation.
When to Use Generated Knowledge
Best for tasks where surfacing the right context improves accuracy
Perfect For
Questions that seem simple but require connecting multiple pieces of everyday knowledge — the original strength of this technique.
When you can’t use RAG or external search, Generated Knowledge lets the model be its own knowledge base — surfacing facts from its training data explicitly.
Questions that span multiple knowledge domains — generating knowledge from each domain first ensures no relevant area is overlooked.
Questions where the answer depends on understanding subtle distinctions — knowledge priming prevents the model from rushing to a surface-level answer.
Skip It When
If you can retrieve actual documents, real-world data beats model-generated knowledge. Generated knowledge is a fallback, not a replacement for verified sources.
The model’s generated knowledge reflects its training data cutoff. For current events or fast-moving fields, the generated facts may be outdated.
Questions with straightforward factual answers don’t benefit from the two-step overhead — “What year was the Eiffel Tower built?” is better answered directly.
Use Cases
Where Generated Knowledge delivers the most value
Educational Assessment
Generate domain knowledge before answering quiz or exam questions, demonstrating understanding rather than pattern-matching. Always verify generated answers against textbook sources before relying on them.
Technical Troubleshooting
Surface relevant system knowledge before diagnosing issues. Having the model recall architecture details, common failure modes, and configuration requirements improves diagnostic accuracy.
Medical Decision Support
Generate relevant clinical knowledge about symptoms, conditions, and treatment protocols before analyzing a case — ensuring no relevant differential diagnosis is overlooked. Always have clinicians verify AI-assisted medical reasoning.
Expert Q&A Systems
In chatbot or Q&A applications, generate domain knowledge before answering user questions to provide richer, more contextual responses than direct-answer approaches.
Fact Verification
Generate background knowledge about a claim before evaluating its truthfulness. The explicit knowledge surface helps identify what facts support or contradict the claim under review.
Strategic Planning
Before generating recommendations, have the model surface relevant market conditions, competitor landscapes, and historical precedents — ensuring strategic advice is grounded in comprehensive context. Cross-reference all generated market data with verified sources.
Where Generated Knowledge Fits
Generated Knowledge bridges direct prompting and retrieval-augmented approaches
Generated Knowledge Prompting is often called a “precursor to RAG.” Both techniques share the same core insight: providing explicit context before answering improves accuracy. The difference is the source. Generated Knowledge uses the model’s own training data; RAG retrieves real documents. When you have access to a knowledge base, RAG is almost always better. But when you don’t — or when the model’s training data is sufficient — Generated Knowledge is a powerful, zero-infrastructure alternative.
A practical hybrid approach: use Generated Knowledge as a first pass, then verify or augment the generated facts with retrieval when possible. This two-layer strategy gives you the breadth of the model’s training data plus the grounding of real documents.
Important: Generated knowledge is only as reliable as the model’s training data. Always treat it as a starting point, not a definitive source. Cross-reference critical facts against trusted, verified sources before acting on them.
Related Techniques
Explore complementary knowledge enhancement techniques
Prime Your Prompts with Knowledge
Try Generated Knowledge on your own questions or build knowledge-enhanced prompts with our tools.