RAG Prompt Cheatsheet - Context-grounded Answers & Hallucination Control
Prompts for retrieval-augmented generation are a different problem from ordinary question answering. In ordinary QA, a model filling gaps from general knowledge is usually right. Once you attach a knowledge base, that same behaviour becomes hallucination: the model reaches for phrasing it saw in training to fill the gaps in the retrieved passages, producing fluent text that cannot be traced back to a source. So the central move in this kind of prompt is not asking a better question but drawing a hard boundary and forcing attribution - wrapping the retrieved text in a tagged block, declaring that only that block may be used, and requiring an explicit insufficiency statement when retrieval came up short. This sheet is organised along five threads: answering from context, citation and fusion, conflict and uncertainty, retrieval quality, and pitfall avoidance. Reach for it when an answer reads smoothly but opening its citations reveals the sources do not support the claim, or when contradictory passages are silently resolved to one side. A second common case is a user asking about something the corpus never covered, where the correct output is a named coverage gap.
Answer From Context 5
Use only the <context> below; no outside knowledge. Q: {q}
<context>{c}</context>Support each claim with <context> verbatim and cite passage numbersIf <context> is insufficient, say "not enough info" — do not guessOne-sentence conclusion first, then evidence with sourcesTurn <context> into a FAQ, each with a source passageCitations & Fusion 5
Append [1][2] to each claim pointing at context blocksSynthesize passages A and B; note agreement and divergenceBuild a timeline from <context> (cite sources)Extract an actionable checklist, source each itemGenerate a glossary; each term cites its contextConflict & Uncertainty 5
If passages conflict, list both and say it cannot be adjudicatedFlag aspects <context> does not cover (coverage gap)Label confidence per claim: high/med/low with reasonSeparate "context states" from "reasonable inference"Cross-check sensitive facts across passagesPairing With Retrieval 5
Instruct the model to ignore irrelevant context blocksAsk it to rerank by relevance before answeringChunk long docs, then ask per chunkHave it name the most relevant blocksAbstain on low relevance rather than fabricatePitfalls 5
Write the context boundary rigidlyVerify citations manuallyPrompt + reranker for recall noiseAbstain beats guessingDon't force-unify conflictsTips
- The soul of RAG prompts is locking the model inside retrieved context — an explicit "answer only from <context>, no outside knowledge" boundary is the first line against hallucination.
- Citing sources (block/passage numbers) makes claims traceable, but AI citations can mis-attach — verify key facts manually.
- Flagging conflicts and confidence tiers beats forced unification; fight recall noise with "ignore irrelevant blocks" prompt + reranker.
- Abstain on low relevance rather than fabricate — holding the line beats completeness.
FAQ
Why does a RAG system answer using knowledge outside the retrieved context?
Because the boundary was never written down — the model fills gaps from training memory, producing fluent answers that cannot be traced. Draw the line explicitly: "answer only from the content in <context>, do not introduce outside knowledge", and require it to "support every conclusion with the original wording and cite the paragraph number". Authorize it to say "information is insufficient" when retrieval falls short; an honest refusal beats a confident fabrication.
Retrieved chunks are noisy — how do I improve answer quality?
Prompting alone is not enough; you need prompts plus a reranker. On the prompt side, require the model to "ignore irrelevant context chunks" and "reorder by relevance before answering". On the system side, add a reranker over the retrieved set. Also improve chunking: for long documents, split first and query chunk by chunk instead of stuffing everything in and getting truncated. Retrieval quality sets the ceiling; prompting only works within it.
What if retrieved passages contradict each other?
Do not let the model force them into agreement. Instruct it to "list the contradictions separately and state that they cannot be adjudicated", and to "distinguish what the context explicitly says from what can be reasonably inferred". Flagging conflicts honestly is more professional than reconciling them, which only yields confident wrong answers. In high-stakes cases, require cross-verification across passages before concluding.
Why require the model to cite sources?
It is both an accuracy safeguard and an entry point for human review. Asking it to "support every conclusion with original wording and cite the paragraph number" gives you a way to verify item by item — follow the number back into the context to confirm the claim. AI citations are occasionally wrong, and spot-checking is expensive; without numbering you cannot quickly isolate the entry that needs review.
How should the system behave when retrieval finds nothing relevant?
It should refuse clearly. Instruct: "if the context is insufficient to answer, say information is insufficient — do not guess", and refuse outright when relevance is low rather than inventing. This looks like a lower answer rate but raises trustworthiness: a retrieval system that admits ignorance is far more dependable in professional settings than one that always answers.
Official References
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Maintained by LaoHand
Publicly updated on Aug 23, 2026, continuously proofread against official docs.
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