To ground an AI agent in current internal documentation, retrieve relevant passages from a maintained corpus, check the user’s permissions before sending those passages to the model, and keep source metadata with every result. Then test whether the right, current evidence is retrieved and whether the answer and its citations actually match that evidence. Retrieval-augmented generation (RAG) supports this workflow; it does not guarantee a correct answer.
How grounding works
In a basic RAG flow, the application searches an index or data store for material relevant to the user’s question, adds selected passages to the model’s input, and asks the model to formulate an answer using that context. This is useful when the needed information is private or changes more often than a model’s training data. Microsoft’s Azure AI Search overview of RAG describes classic and agentic patterns, retrieval options, citations, and security considerations.
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The model can only use what the retrieval step finds and what the application supplies. If a document is missing, outdated, poorly split, or ranked below less relevant material, the answer may be incomplete or wrong even when the system uses RAG.
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Organize and chunk the corpus
Index documents in a form that allows relevant sections to be retrieved independently. Large files may need to be divided into meaningful chunks; chunk boundaries should preserve enough context for a passage to make sense on its own. Tune this preparation for the actual document types and questions rather than assuming one chunking approach works for everything.
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Keep useful metadata alongside each chunk, such as the document title, URL or file name, document ID, and date or version information where available. Those fields help the application identify sources, distinguish revisions, and build accurate citations.
Choose retrieval for the questions people ask
Keyword search can help when questions use exact terms, names, or identifiers. Semantic or vector retrieval can find conceptually related content even when wording differs. Hybrid retrieval combines keyword and vector search; Azure AI Search documents hybrid query patterns and semantic ranking. The right configuration depends on the corpus and question types, so assess it against representative queries rather than treating any one method as universally best.
Keep retrieved information current
Current answers depend on two separate things: the source of record must be current, and the retrieval system must have ingested or connected to the current version. Incremental indexing can update an index as content changes, but it cannot make an outdated source document correct. Microsoft’s documentation on RAG and indexes in Microsoft Foundry discusses indexing, freshness, and testing; for a clean publication URL, refer to the same Microsoft Foundry documentation by title without embedding its tracking parameter.
- Preserve version, publication, or effective-date metadata when the source provides it.
- Remove obsolete copies or make it clear which document supersedes them, so an old version does not compete with the source of record.
- After a policy or procedure changes, test whether the new version appears in search and the obsolete version no longer wins for relevant questions.
- Where multiple results remain valid, freshness-aware ranking may help prefer a newer one, but ranking cannot repair stale source material.
Make retrieval available to the agent as a controlled tool
For an agent, describe the retrieval tool clearly: identify which corpus it searches and what inputs or parameters it accepts. Return a limited set of useful passages rather than an unbounded document dump, and attach source titles, dates, document IDs, and relevance scores. Keep the passages tied to their metadata so later answer generation can cite the actual source.
A fixed pipeline can send one query to retrieval and pass the results to the model. For harder questions, agentic retrieval can break a question into focused searches, run them across sources, assess whether the results are sufficient, and search again when needed. The Azure Architecture Center’s guide to developing an agentic RAG solution describes retrieval-as-tool design and iterative retrieval flows. Additional planning can improve coverage for complex questions, but it also adds orchestration and operational complexity; verify feature availability before relying on preview capabilities.
Choose classic RAG or agentic retrieval
| Approach | How it works | Good fit when | Trade-off |
|---|---|---|---|
| Classic RAG | A single query is sent to retrieval, and selected passages are supplied to the model. | Questions are relatively direct, the corpus is manageable, and a simple, controlled retrieval handoff is sufficient. | It may not gather enough evidence for complex questions requiring multiple searches or follow-up context. |
| Agentic retrieval | The agent plans and executes multiple focused searches, evaluates results, and can repeat retrieval when context is insufficient. | Questions need decomposition, sources vary, or the agent must gather and assess evidence iteratively. | Planning and repeated tool use add complexity; latency, cost, metadata needs, control, and feature availability matter. |
Choose based on the actual workload: question complexity, source count and variety, citation needs, latency, cost, operational capacity, and how much control you need over retrieval. The cited Microsoft material describes the patterns, not a vendor-neutral benchmark establishing a universal winner.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Enforce permissions before evidence reaches the model
Apply authentication and authorization at the retrieval boundary. The system must not return a passage from a document the requesting user is not allowed to access; relying on a natural-language instruction telling the model to ignore restricted material is not an access-control mechanism. Filter results using the user’s permissions before constructing the model input.
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Keep answers traceable and evaluate them
Return citations tied to the passages used, including source title and a URL or document identifier, plus dates or version details when available. A citation trail makes an answer reviewable, but a citation alone does not show that the cited passage supports every statement. Check that sources are correctly identified and that each cited passage actually supports the answer it accompanies.
Evaluate retrieval and generation separately as well as together. Build representative questions from real internal workflows, including questions about changed policies and questions where the corpus lacks a reliable answer. Check:
- Retrieval relevance: Are the returned passages about the question, rather than merely sharing a term?
- Coverage: Do the results contain the facts needed to answer fully, including relevant details spread across documents?
- Freshness: After a source changes, does retrieval return the updated version instead of an obsolete copy?
- Answer accuracy: Does each material claim follow from the retrieved evidence, without unsupported additions?
- Citation correctness: Do the titles, identifiers, dates, and cited passages point to the sources that support the claims?
- Abstention: When evidence is missing, conflicting, or insufficient, does the agent say so rather than inventing an answer?
- Access control: Do permission tests confirm that users cannot retrieve documents outside their authorization?
Microsoft Foundry documentation summarizes the dependencies this way: “RAG quality depends on content preparation, retrieval configuration, and prompt design.” Treat grounding as an end-to-end system property: content operations, search, access control, answer generation, citations, and ongoing evaluation all contribute.
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