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RAG guide · 12 min

Build a reliable, citable RAG knowledge base

Connecting AI to documents does not automatically make answers accurate. Quality depends on corpus governance, permissions, chunking, retrieval, citations and tests that include questions with no answer.

Updated 21 September 2026Read and editorially checked by Pictoquest
An information specialist organises versioned sources connected to an AI answer with visible citations.
Key points

The short answer

Start with a limited corpus and a named owner. Remove stale versions, retain source metadata and permissions, test retrieval separately from generation, display cited passages and teach the system to say when the corpus is insufficient.

  • Govern the corpus
  • Test retrieval separately
  • Make citations verifiable

Turn files into usable knowledge

  1. 1. Define the scope

    List users, allowed questions, included documents and excluded decisions. Assign a corpus owner and an update frequency.

  2. 2. Prepare sources

    Remove duplicates and obsolete versions; retain title, author, date, language and access level. Check extraction from tables, notes, scans and attachments.

  3. 3. Enforce permissions

    Retrieval must never bypass document rights. Apply controls before retrieval, log useful access events and test with several user profiles.

  4. 4. Tune retrieval

    Select chunking, metadata, lexical or vector search and passage count for the documents. First measure whether the right passages are found, independently from final prose.

  5. 5. Produce a traceable answer

    Show source title, version and supporting passage. Separate quotation from synthesis, prohibit invented references and return an explicit insufficiency message when evidence is weak.

  6. 6. Evaluate and maintain

    Test simple, ambiguous, contradictory, outdated and out-of-scope questions. Track retrieval precision, supported answers, permission failures, freshness and correction time.

Four layers to control

Corpus

Are documents correct, current, readable and attributed?

Access

Does each person retrieve only material they may view?

Retrieval

Do useful passages rank above merely similar passages?

Answer

Is every material claim supported and easy to verify?

6 starting points

Document tools, platforms and local options

These families cover source-grounded notebooks, enterprise platforms and local components. Verify connectors, permissions, data residency and citation mechanisms.

How is this selection produced?

Active services are distributed across guide-related categories, then ordered by editorial highlighting and internal score. This does not assess security, compliance or performance on your use case. Methodology.

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Frequently asked questions

How does RAG differ from training?

RAG retrieves passages at answer time without necessarily changing the model. Training adjusts behaviour or parameters and requires a different data and evaluation lifecycle.

Is a vector database always required?

No. Lexical search can work better for exact references. Hybrid retrieval is often useful, but it should be justified by tests on the real corpus.

How should expired documents be handled?

Keep a validity date, owner and archival rule. Remove the version from the active index while retaining records when documentary obligations require them.

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