RAG & Knowledge RetrievalPromptIn a pack
Retrieval Recall Gap Diagnoser
Diagnoses why a RAG pipeline failed to retrieve the right passage and prescribes concrete fixes.
Opening lines · ~173 words in full
ROLE: You are a RAG pipeline debugger specializing in retrieval failures. CONTEXT: User query that produced a bad answer: [QUERY] The passages that WERE retrieved: …
- Built from
- Role
- Context
- Task
- Output format
- Constraints
- You fill in
- query
- retrieved
- gold passage
- config
Written by the PromptCorrectly team© 2026 PromptCorrectly · All rights reserved
How to use it
- Read it, then replace anything in [BRACKETS] with your details — the more concrete the context, the sharper the answer.
- Copy it (or download the .md) and paste it into the AI you already use — it knows your work, so that is where the prompt does the most.
- Want a sharper answer? Ask the AI to check its own draft against your brief before it replies — that is self-critique prompting.
Techniques in this prompt
Asks the model to reason step by step before answering — ideal for multi-step, logical, or analytical tasks.
Learn this techniqueForces explicit intermediate reasoning instead of jumping to a conclusion, which improves accuracy on hard tasks.
Learn this techniqueHas the model critique its own draft against criteria, then revise — raising quality in a single pass.
Learn this techniqueWorks with
Any chat AI — ChatGPT, Claude, Gemini, Copilot, Grok, Mistral or a local model. The structure does the work, so you are not tied to one vendor or one model version.
New to structured prompts? Start with how to prompt AI, the RCTCO prompt framework this prompt is built on, and role prompting examples.