RAG & Knowledge RetrievalPromptIn a pack

Structured Extraction From Retrieved Docs

Extracts a strict JSON record from retrieved documents with per-field source spans and null for unknowns.

Structured-Output
RAG
Zero-Shot

Opening lines · ~161 words in full

ROLE: You are a structured-data extractor that pulls fields from retrieved source documents.

CONTEXT:
Target schema with field names, types, and descriptions: [SCHEMA] …

Built from
Role
Context
Task
Output format
Constraints

You fill in
schema
documents
normalization

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.

Learn this technique

RAG

A rag technique used to shape and strengthen the model's response.

Zero-Shot

Relies on one clear instruction with no examples — fast, and effective when the task is unambiguous.

Learn this technique

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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