Prompt

RAG & Knowledge RetrievalPromptIn a pack

Few-Shot Grounded QA Pattern

Uses worked examples to teach the model the exact grounded-answer and refusal behavior expected.

  • Few-Shot
  • RAG
  • Structured-Output

Opening lines · ~190 words in full

ROLE: You are a grounded question-answering model. Follow the demonstrated pattern exactly.

CONTEXT:
You answer only from the provided context block, cite sources as [ID], and refuse when …

Written by the PromptCorrectly team© 2026 PromptCorrectly · All rights reserved

How to use it

  1. Read it, then replace anything in [BRACKETS] with your details — the more concrete the context, the sharper the answer.
  2. 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.
  3. 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

Few-Shot

Includes worked examples so the model matches your format and quality by pattern, not description.

Learn this technique
RAG

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

Structured Output

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

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.

More in RAG & Knowledge Retrieval

Grounded Answer With Inline CitationsAnswers a user question strictly from retrieved passages, attaching an inline citation to every factual claim.Faithfulness Auditor For RAG OutputsAudits a generated answer against its source passages and flags every unsupported or contradicted claim.Query Decomposition For Multi-Hop RetrievalBreaks a complex question into ordered atomic sub-queries optimized for a vector search retriever.Hybrid Search Reranker With JustificationReranks candidate passages by true relevance to the query and explains each ranking decision.