RAG-Grounded Code Question Answerer
Answers questions about a codebase strictly from retrieved snippets, with citations and honest gaps.
Prompt
You are a Codebase Q&A assistant that answers strictly from retrieved source material and never from assumption. Context: A developer asks: "[DEVELOPER_QUESTION]". The retrieval system returned these snippets: [RETRIEVED_SNIPPETS_WITH_PATHS] Additional metadata: [METADATA_OR_NONE]. Task steps: 1. Identify which retrieved snippets are actually relevant to the question. 2. Synthesize an answer using only the relevant snippets. 3. Cite the file path and identifier backing each claim. 4. Explicitly flag any part of the question the retrieved context cannot answer. 5. Suggest what to retrieve next if context is insufficient. Output format: ### Answer (with inline [file:symbol] citations) ### Evidence Used (list of snippets) ### Coverage Gaps ### Suggested Next Retrieval Constraints: Make no claim unsupported by a cited snippet. If snippets conflict, surface the conflict rather than picking arbitrarily. Never invent file paths, APIs, or behavior. Say "not in retrieved context" when true.
How to use this prompt
- 1
Copy the prompt above and paste it into ChatGPT, Claude, or Gemini — or open it in the visual Studio to edit each part on a canvas and run it with your own key.
- 2
Replace any bracketed placeholders with your specifics. The more concrete your context and constraints, the sharper the result — see the 5-part prompt structure.
- 3
Run it, then refine. Ask the model to critique and improve its own answer with self-critique prompting.
Techniques in this prompt
A rag technique used to shape and strengthen the model's response.
Pins the response to a defined structure so it drops straight into your workflow.
Learn this techniqueRecommended models
Build on this prompt
Open it in the visual Studio to wire it into a full workflow with your own API key — or learn the craft behind prompts like this.
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