Keep an AI agent grounded by retrieving relevant passages from maintained internal documents for each question, checking the requesting user’s permissions before those passages reach the model, and returning source details with the answer. Then test whether retrieval finds the right, current documents and whether the generated answer accurately reflects them. Retrieval-augmented generation (RAG) supports this workflow, but does not guarantee a correct answer.
What grounding an AI agent in internal documentation means
Retrieval-augmented generation adds a retrieval step to answer generation. The application searches an internal index or data store for relevant content, includes selected passages in the model’s input, and asks the model to answer using that context. This can make private information and material that changes more often than model training data available to the agent.
A typical flow is user question → retrieval → context added to the prompt → generated answer. The model still has to interpret the retrieved material. If search returns irrelevant or incomplete passages—or the answer misreads them—the response can still be wrong or incomplete. Microsoft’s Azure AI Search overview of RAG describes both the retrieval patterns and the preparation and security considerations involved.
How to build a grounded documentation workflow
- Choose and maintain the source of truth. Identify which repositories contain authoritative policies, procedures, product documentation, or other material the agent is allowed to use. Keep the source documents current; an index cannot correct outdated source content.
- Prepare documents for retrieval. Organize and split long documents into passages that can be retrieved independently. Keep useful context with each passage, including titles, document IDs, file names, URLs, and dates or version information where available. Poorly chosen chunks can separate a statement from its qualifications or make a passage hard to identify later.
- Index the content and choose a search approach. Use keyword search, semantic search, vector search, or a combination based on the corpus and the kinds of questions users ask. Hybrid search combines keyword and vector search; semantic ranking can be used in documented Azure AI Search patterns. Treat these as options to evaluate, not a guarantee that one approach fits every collection.
- Retrieve a focused set of passages for each question. Pass the question and the most relevant results to the model rather than relying on a broad document dump. Include source metadata alongside the text so the application can identify where each passage came from.
- Instruct the model how to use the evidence. Make clear that retrieved passages are the evidence for the answer, and define what to do when they do not contain enough information—for example, say that the documentation does not establish an answer rather than filling the gap with a guess. Prompt instructions help, but they do not replace retrieval controls or evaluation.
- Return traceable answers. Associate claims with the retrieved passages and their source titles, links, dates, or document IDs. Keep the passage-to-source mapping in the application so citations point to the actual retrieved evidence rather than a guessed or reconstructed URL.
Microsoft’s Azure Architecture Center guide to agentic RAG describes exposing retrieval as a clearly specified tool, including the corpus it searches and the parameters it accepts. Its examples also support returning source metadata so answers can be traced back to documents.
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How to keep retrieved information current
“Current” depends on two separate things: the source document must be up to date, and the retrieval system must have ingested or connected to the current version. A recently refreshed index can still contain stale information if the source of record is stale. Conversely, a corrected source document will not help the agent until the updated content is available to retrieval.
Maintain the source and its version history
- Preserve effective dates or version identifiers where the source provides them.
- Remove obsolete copies or clearly mark them as superseded so search does not return an old policy alongside its replacement without context.
- Decide which source takes precedence when multiple documents cover the same subject.
Refresh the index and verify the result
Incremental indexing is one documented way to keep indexed content fresh, and freshness-aware ranking can help distinguish newer material from older results. Neither approach makes an outdated source correct. After a document changes, test whether the new version appears in retrieval and whether the obsolete version is excluded or clearly superseded. Microsoft’s Azure AI Search RAG documentation discusses incremental indexing and retrieval options.
How to enforce permissions and handle unsafe retrieved text
Apply authentication and authorization at the data boundary, during retrieval. The system must not send the model passages the requesting user is not allowed to access. Filtering only after generation is too late: unauthorized content may already have influenced the response.
Retrieved text is also untrusted input. A document can contain text that attempts to override the agent’s instructions or redirect its behavior. Do not treat internal documentation as inherently safe just because it is internal. Use application logic and system instructions to reduce prompt-injection risk, while keeping permission checks outside the model’s control. Microsoft’s RAG guidance covers security controls and the need to treat retrieved content carefully.
- Authenticate the user and apply that user’s document permissions before selecting passages.
- Keep access-control metadata connected to indexed content so retrieval can filter results correctly.
- Do not rely on a natural-language instruction telling the model not to reveal inaccessible information.
- Test with users who have different access rights, including cases where the answer exists only in a restricted document.
When to use classic RAG or agentic retrieval
A fixed retrieval pipeline is often the simpler starting point: one query retrieves a set of passages, then the model answers from them. Agentic retrieval lets an agent plan multiple focused searches, search across sources, assess what it found, and repeat when context is insufficient. That added flexibility also brings more orchestration and operational complexity.
| Approach | How retrieval works | Useful when | Trade-off |
|---|---|---|---|
| Classic RAG | A single-query retrieval handoff supplies passages to the answer-generation step. | The question and corpus are served well by a straightforward search, and simpler orchestration is preferred. | It may not handle complex questions that need several searches or follow-up context as naturally. |
| Agentic retrieval | The agent can decompose a question, call retrieval across sources, assess results, and search again. | Questions require multiple focused searches, diverse sources, or iterative context gathering. | Planning and tool use add complexity; latency, cost, control, citation metadata, and feature availability need consideration. |
Choose based on the real workload: question complexity, source variety, need for query decomposition, latency and operational constraints, and how much control you need over retrieval. The cited Microsoft documentation describes these patterns, but does not establish a vendor-neutral benchmark or a universal winner.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to evaluate whether answers are actually grounded
Test retrieval and answer generation separately as well as together. A fluent answer is not evidence that the right documents were found, and a citation is not proof that the cited passage supports every sentence.
Test retrieval
- Use representative questions from the document types and tasks people actually bring to the agent.
- Check whether the results contain the passages needed to answer, not merely documents with similar wording.
- For changed policies or procedures, verify that the updated version is retrieved instead of an obsolete one.
- Test queries that require different retrieval modes, such as exact names or terms as well as questions phrased in everyday language.
Test generated answers and citations
- Check whether the answer is accurate, complete, and supported by its cited passages.
- Verify citation correctness: the title, link, date, or document ID should identify the source of the passage actually used.
- Test whether the agent acknowledges when retrieved context is insufficient instead of inventing missing details.
- Include permission-boundary and prompt-injection cases in security testing.
Repeat these checks when documents, indexing behavior, retrieval configuration, or prompts change. Grounding is an end-to-end property of the content, retrieval, permissions, and answer behavior—not a setting that can be enabled once and assumed to work.
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