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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware matchRetrieval-augmented generation (RAG) can reduce unsupported answers by giving an AI model relevant enterprise evidence to use. It cannot guarantee that an answer is true: the system may retrieve the wrong material, miss the right material, or misinterpret what it finds. Reliable results come from improving and evaluating the entire evidence pipeline—not from adding search or changing a prompt alone.
What RAG can—and cannot—do about hallucinations
In a RAG system, a search step finds material relevant to a question and supplies it to a language model as context. The model then generates an answer using that context. This is useful when a model needs to answer from changing or organization-specific information rather than rely only on what it learned during training. Microsoft’s RAG design and evaluation guidance treats this as a system-design problem, not a single model feature.
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RAG changes the conditions under which an answer is produced; it does not prove the answer. A response can still be wrong if retrieval returns stale or irrelevant passages, if the needed information is absent, or if the model draws an invalid conclusion from otherwise relevant text. Google likewise describes grounding as using retrieved information to inform a response, not as a guarantee of correctness in its grounding overview.
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Where unsupported answers enter the pipeline
A useful diagnosis follows the answer from source material to production monitoring. Each stage can introduce a different failure, so a wrong answer should not automatically be blamed on the language model.
- Source material: The authoritative document may be missing, outdated, contradictory, or inaccessible to the system.
- Parsing and chunking: Extraction can lose context, and a passage split away from its headings or qualifications may become hard to interpret.
- Indexing and retrieval: Search may fail to find the relevant passage or rank unrelated material highly.
- Context assembly: The system may provide too little evidence, include confusing duplicates, or fail to make source boundaries clear.
- Generation: The model may overstate what a passage says, combine sources incorrectly, or fill gaps with plausible-sounding detail.
- Evaluation and monitoring: If teams inspect only final answers—or only a single score—they may miss the stage that caused the failure.
Microsoft’s RAG solution guide separates document preparation, search strategy, retrieval evaluation, and end-to-end evaluation because these are distinct design concerns.
Build a trustworthy enterprise knowledge base
Start with the material the system is allowed to use. Select authoritative documents that are current for the intended task, and maintain enough metadata to identify their owner, version, and freshness. Preserve access permissions: retrieving a document a user should not see is a governance failure even if the answer is factually accurate. Google’s RAG overview describes grounding answers in organizational data; the specific governance design still needs to fit the organization and its data.
Then inspect how source files become searchable evidence. Check whether parsing preserves tables, headings, dates, and qualifications. Review chunks in context rather than assuming that a technically successful indexing job produced useful passages. Test search and chunking choices against representative questions from the people and workflows that will use the system. Microsoft’s design guidance treats preparation and retrieval choices as parts of the solution that need evaluation.
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- Can the system find the latest approved version of a policy?
- Does a retrieved passage retain enough surrounding context to interpret it correctly?
- Can an evaluator identify which source and version supported an answer?
- Are access rules respected throughout retrieval and answer generation?
- What should happen when sources disagree or the corpus does not contain an answer?
Test retrieval before tuning the model
Create a representative set of real questions, including difficult cases such as questions with no answer in the corpus, questions whose answer depends on a date or exception, and questions where documents conflict. For each query, inspect the retrieved passages and ask whether they contain the evidence needed to answer it. This isolates retrieval problems from generation problems.
Keep traces of retrieved items alongside the question and final response. If the right evidence never reaches the model, prompt edits are unlikely to fix the underlying problem. If relevant evidence is present but the response misstates it, investigation can move to context assembly, instructions, and generation. Microsoft’s evaluation and monitoring guidance recommends evaluating retrieval and monitoring the application, rather than treating the generated answer as the only observable result.
When retrieval underperforms, revisit the source preparation, chunking, indexing, and search strategy, then run the same test questions again. Record what changed and compare the retrieved evidence as well as the final answers. A change that makes responses sound more confident is not an improvement if it makes them less supported.
Give the model clear evidence and fallback rules
Prompts should explain the role of the supplied context, what to do when it is insufficient, and how to handle conflicts. They should also specify the expected response format when that helps users check the result. For example, an enterprise assistant could be instructed to answer from the supplied passages, distinguish facts from inferences, say when the passages do not establish an answer, and identify a conflict rather than silently choosing a side.
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Those are policy choices, not magic wording. A conflict rule might direct the system to prefer a designated authoritative source or to report both sources and request review; the right choice depends on the organization’s rules. Keep passages clearly separated and label source information so that the model can distinguish one document from another. Microsoft’s RAG prompt engineering guidance covers structuring prompts and context, but prompt changes should be tested against representative cases rather than assumed to work.
Check specifically that the model does not turn a missing answer into a guess, treat a dated policy as current without support, or present an inference as a directly stated fact. If users need citations, provide source references in a form they can inspect and verify; a citation is useful only when it actually supports the associated claim.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Evaluate groundedness and correctness separately
Groundedness asks whether the response is supported by the retrieved context. Correctness asks whether the response is actually right. The two are related but not interchangeable: a model can cite a relevant passage and still reason incorrectly, while a correct answer may not be supported by the evidence the system supplied. Add other dimensions where they matter so that one attractive score does not hide a different failure.
| Evaluation dimension | Question to ask | What a failure may reveal |
|---|---|---|
| Groundedness | Does the supplied context support the response’s claims? | The model added unsupported details, or the context was not sufficient for the answer. |
| Correctness | Is the response factually right for the task? | The model may have misread evidence or made an invalid inference, even if it cited relevant text. |
| Completeness | Does the response cover the material parts of the question? | Retrieval may have omitted evidence, or the response may have overlooked an important qualification. |
| Relevance | Does the response address the user’s question? | The system may have retrieved or emphasized material that does not answer the query. |
| Utilization | Did the answer make appropriate use of the supplied evidence? | The model may have ignored useful context or relied on only part of it. |
Build a curated evaluation set with expected evidence and, where appropriate, reference answers. Review retrieval quality and end-to-end responses separately, and retain experiment settings and results so changes can be compared. Microsoft’s end-to-end evaluation guidance discusses multiple evaluation dimensions; its monitoring and evaluation guidance also addresses tracing RAG applications.
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Use grounding checks as one layer of review
One vendor-specific example is Google’s grounding-check API. Its documentation says the API compares a candidate answer with reference facts, returns a support score and citations to supporting facts, and allows citation thresholds to filter answers considered likely to be ungrounded. Google defines perfect grounding as every claim being supported by one or more facts. These checks can help flag unsupported answers, but a grounding score does not establish that a response is factually correct or complete. Validate the API’s behavior and any threshold against the actual workload before relying on it, especially in high-impact workflows. See Google’s grounding-check documentation.
Monitor production and feed failures back into evaluation
Production questions and documents change. Retain enough trace information—subject to the organization’s privacy, security, and retention rules—to connect an input and output with the intermediate retrieval results that informed it. That evidence lets a team distinguish a retrieval miss from a generation error and review consequential failures with subject-matter experts.
Turn newly observed questions and reviewed failures into additions to the evaluation set. Repeat evaluations when the corpus, user questions, retrieval configuration, prompt, or model changes. Microsoft’s evaluation and monitoring guidance and evaluation guidance support ongoing monitoring and repeated assessment rather than a one-time launch check.
The Tool Desk
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There is no neutral winner established by the vendor guidance reviewed here. For a hosted service or a custom architecture, compare options against the same real questions and operational constraints. Useful buyer evaluation axes include:
- Evidence and corpus connectivity: Can it access the approved sources and return material that answers the intended questions?
- Retrieval and evaluation visibility: Can your team inspect retrieved passages, trace failures, and evaluate retrieval separately from generation?
- Access control and governance: Can the design meet requirements for permissions, data handling, freshness, and document ownership?
- Operational fit: What latency, cost, and maintenance burden does it produce under the organization’s actual workload?
Microsoft’s RAG design guide and Google Cloud’s RAG reference architecture describe implementation approaches, not a controlled comparison proving one approach superior. Measure the options in the target environment before selecting one.
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