Trace the answer from its source through ingestion, retrieval, generation, and display. An outdated or irrelevant answer is not automatically a model problem: the authoritative record may be stale, the index may not contain its latest revision, retrieval may select the wrong passages, or the answer may fail after good evidence reaches the model. Inspect each stage before changing prompts or models.
Trace one failing answer end to end
Retrieval-augmented generation (RAG) retrieves material from an index or data store, adds it to the model’s input as grounding, and asks the model to answer. That lets an agent use private or changing information that may not have been in its training data, but grounding does not guarantee correctness: the result still depends on the source, ingestion, retrieval configuration, instructions, and rendering.
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For one specific bad answer, preserve the user’s exact question and relevant conversation context, then follow this path:
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1Clear out junk files and repair common Windows errors2Scan for outdated or missing drivers - takes under a minute3Repair Windows errors before they cause bigger problems- Question and context: Record the wording, conversation history, user identity, and time of the request.
- Source: Identify the document or record that should have grounded the answer, including its owner, revision, and effective date.
- Ingestion: Check whether that revision was synchronized and whether the index reflects it.
- Retrieval: Capture the actual passages, metadata, filters, and scores returned for the request.
- Generation: Compare those passages with the context sent to the model and the answer it produced.
- Display: Check whether the final UI shows the answer and citations accurately.
This trace follows the RAG workflow described in Microsoft’s “Retrieval augmented generation (RAG) and indexes in Microsoft Foundry” and the stages in Microsoft’s “Grounding and Response Quality Remediation” runbook. A stage-by-stage trace helps distinguish a retrieval failure from an answer-generation or display failure.
Is the source of truth actually current?
Start by confirming what the correct answer should be. Find the authoritative document or record, check its owner and effective date, and look for conflicting copies, archived policies, or superseded versions still available to the agent. If the source itself is obsolete or contradictory, changing retrieval settings cannot establish the right answer.
Use a distinctive phrase from the expected current document to search the underlying source system. This establishes whether the material exists where the connector is supposed to read it before you investigate the index.
Did ingestion put the current revision in the index?
If the phrase is missing from the source system, check connector scope, source permissions, synchronization status, and ingestion errors. If it exists in the source but is absent or stale in the index, investigate the ingestion pipeline and compare the indexed last_modified metadata with the source revision.
- Confirm the connector includes the correct site, folder, repository, or record type.
- Inspect synchronization and ingestion logs for failures, delays, or skipped files.
- Check whether the indexed item is the expected revision rather than a duplicate or superseded copy.
- Verify that modification dates are present, accurate, and mapped consistently into index metadata.
Do not treat a successful connector status as proof that a particular document was indexed correctly; verify the item and its metadata.
Did retrieval find the right evidence?
Capture the passages returned for the exact failing query before changing the prompt. If the expected document is missing, inspect how the query is interpreted, which filters apply, whether chunks preserve the relevant context, and how the system combines keyword, semantic, vector, or hybrid search. Where configured, examine reranking as well. Microsoft’s RAG guidance describes these retrieval choices and their role in troubleshooting.
If old material appears ahead of the current version, first verify that dates are attached to the right indexed items and consistently understood by the retrieval system. A relevance score alone does not establish that a passage is current or authoritative. Also check whether a filter or access rule excludes the correct document.
Choose freshness controls based on the time requirement
Recency ranking and strict time limits solve different problems. Microsoft documents freshness-aware retrieval for Azure AI Search as a preview feature tied to REST API version 2026-08-01-preview. It biases ranking toward newer indexed material; it is not a hard cutoff, so a strongly relevant older passage can still be returned. For a question that must be answered only from a defined date range, use an explicit date filter or query a live source instead.
The Azure feature’s freshness field is generated during ingestion. Content ingested before the policy may lack that signal. After applying the policy and ingesting content, compare results containing both old and new versions; if behavior is unexpected, inspect last_modified. Microsoft’s documentation also says the policy cannot be removed from an existing knowledge source without recreating that source, so account for that implementation constraint before enabling it.
If retrieval is good, check generation and citation display
When the retrieved passages are appropriate, check whether the model actually received them. Review the assembled prompt or request context for truncation, missing passages, and instructions that conflict with source-grounded answering. Test whether the agent handles follow-up questions by retrieving again or instead relies on conversational history that may no longer be current.
Then compare the model’s raw output with what users see. Microsoft’s remediation runbook notes that strict output formats can interfere with citation markers and that custom rendering must display citations itself. If the answer is grounded but citations disappear, investigate formatting and UI rendering rather than retrieval.
Compare results across users and access contexts
Reproduce the same question with an affected account and an account that gets the expected answer. Differences can reflect permissions, licensing, region, staged connector rollout, or stale identity mappings. Confirm which evidence each identity is allowed to retrieve; a privileged test account does not prove ordinary users can access the same passages.
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Apply access controls at retrieval time and test with representative user identities. This helps distinguish an authorization difference from a relevance issue while preserving the intended document boundaries.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Use a data query when the question is not a document-retrieval task
Document retrieval returns selected passages. It is not a dependable substitute for exact aggregation, joins, exhaustive lists, or live record status. Match the implementation to the question:
| Question or need | Better fit | Why |
|---|---|---|
| Explain a policy or summarize a known set of documents | Document retrieval with source-aware citations | The answer can be grounded in relevant text passages. |
| Calculate totals, compare joined records, or return an exhaustive list | Database action, BI system, or data-warehouse query | Structured queries are designed for exact filtering and aggregation. |
| Report current status that changes frequently | Live source-system query or real-time connector/action | An indexed document may lag behind the current record state. |
These approaches can be combined. In “Inside OpenAI’s in-house data agent,” OpenAI describes using live warehouse queries when existing context is stale, illustrating how institutional knowledge retrieval can coexist with runtime data access.
Evaluate fixes against repeatable cases
Keep a fixed set of real user questions with the expected source passages and expected behavior. Include cases where the correct response is “I don’t know,” as well as permission-sensitive questions. Re-run the same cases after content migrations, connector changes, prompt edits, or model changes so that a local improvement does not conceal a regression elsewhere.
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Separate retrieval quality from answer quality. AWS’s “Evaluate the performance of RAG sources using Amazon Bedrock evaluations” distinguishes retrieve-only evaluation from retrieve-and-generate evaluation. First check whether the correct evidence was retrieved; then assess whether the generated answer used it faithfully. This narrows whether a change belongs in indexing and retrieval or in generation.
Choose retrieval approaches with operational trade-offs in mind
When comparing retrieval designs, assess more than relevance scores. Microsoft’s Foundry guidance contrasts classic RAG, which avoids LLM query planning and is simpler and faster, with agentic retrieval, which can use conversation-aware planning, multiple focused subqueries, structured grounding data, citations, and execution metadata. The added planning can help with complex questions, but the choice should reflect the query, observability, access model, and latency requirements.
Quick Recap
- Freshness semantics: Is content periodically indexed or fetched live? Does recency boost ranking, or does a filter enforce a date boundary?
- Relevance controls: Are keyword, semantic, vector, or hybrid retrieval, metadata filters, and reranking configured for the question types users ask?
- Evidence quality: Can you inspect source provenance, citations, chunk completeness, and stale-version handling?
- Permissions: Are user-level access controls applied when evidence is retrieved?
- Observability and evaluation: Can operators inspect retrieved chunks, query plans, errors, and repeatable test results?
- Operational constraints: What latency and operating cost does the selected approach add?
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