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Prompt and RAG quality

Prompt and RAG Quality Kit

Check whether prompts, retrieved chunks, schemas, and AI answers are safe, useful, and verifiable before putting an agent workflow into production.

Finish line: A prompt and RAG quality brief with injection risks, weak chunks, schema gaps, context pressure, similarity issues, and hallucination warnings.

Start the reviewed route Ask Tool Finder

Best route through FastTool

These links are intentionally ordered. The goal is not to browse hundreds of utilities; it is to finish one valuable job with enough evidence to trust the output.

Prepare RAG chunks Start here before adding content to a retrieval index. Scan prompt injection risk Look for override attempts, tool-control language, hidden instruction patterns, and data exfiltration clues. Score chunk quality Check whether each chunk is retrieval-friendly, self-contained, well-sized, and answerable. Extract output schema Turn example LLM output into a schema and find missing required fields. Score hallucination risk Flag unsupported dates, people, prices, named entities, and citation gaps.

Tools used in this kit

Prompt Injection Scanner Find prompt injection patterns in retrieved text, user messages, and tool outputs. RAG Chunk Quality Scorer Score chunk size, headings, entity preservation, ambiguity, overlap, and query coverage. Embedding Similarity Explorer Find near-duplicate chunks and term overlap before indexing. LLM Output Schema Extractor Infer JSON Schema from sample output and expose missing contract fields. Context Window Budgeter Estimate retrieval, prompt, history, and output pressure before the workflow overflows. AI Hallucination Likelihood Scorer Score unsupported specificity before publishing or trusting an answer.

Safe sample and expected output

Safe sample

Five retrieved document chunks, one system prompt, one user task, one expected JSON output, and one model answer that contains dates, prices, named products, and no citations.

Expected deliverable

A prioritized quality brief listing unsafe instructions, weak or duplicated chunks, missing schema fields, context pressure, unsupported claims, and a safer rerun prompt.

Proof checks before you trust the result

  1. Scan retrieved text for instructions that try to override the system or developer message.
  2. Reject chunks that are too tiny, too broad, duplicate-heavy, or missing the entities needed to answer.
  3. Require a schema or output contract before accepting structured AI output.
  4. Budget context before adding more retrieval chunks.
  5. Mark unsupported named claims as verification work, not facts.

Realistic use cases

Support bot owner

Checks help-center chunks before letting an agent answer customers.

Developer

Adds a schema contract before wiring LLM output into automation.

Publisher

Scores an AI-generated answer before publishing it as page copy.

Common mistakes this kit prevents

  • Indexing every paragraph without checking whether chunks answer real questions.
  • Allowing retrieved text to control tools or override instructions.
  • Trusting a confident answer with named claims and no source trail.
  • Ignoring context pressure until the agent silently drops key instructions.
  • Testing only the prompt while ignoring retrieved data quality.

When not to use this kit

Bypassing model safeguards, production security approval, regulated compliance sign-off, legal or medical fact checking, or approving a private agent architecture by itself.

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