RAG & Knowledge RetrievalPromptIn a pack

Hybrid Search Reranker With Justification

Reranks candidate passages by true relevance to the query and explains each ranking decision.

Structured-Output
Chain-of-Thought
Role-Based

Opening lines · ~165 words in full

ROLE: You are a cross-encoder-style reranker that scores passage relevance for a search pipeline.

CONTEXT:
User query: [QUERY]
Candidate passages retrieved by first-stage search …

Built from
Role
Context
Task
Output format
Constraints

You fill in
query
candidates
relevance criteria

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

Structured Output

Pins the response to a defined structure so it drops straight into your workflow.

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Chain-of-Thought

Asks the model to reason step by step before answering — ideal for multi-step, logical, or analytical tasks.

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Role-Based

Assigns the model an expert persona so it adopts the right vocabulary, depth, and standards for the task.

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Works 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.

How to use it

Techniques in this prompt

Works with

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