Retrieval-augmented generation (RAG) can reduce unsupported answers by giving an AI model relevant company information to use as evidence. It cannot guarantee accuracy: the system may retrieve the wrong material, miss the right source, or draw a faulty conclusion from what it finds. The practical way to reduce hallucinations is to build and test the entire evidence pipeline—from document quality and search to the model’s answer—and keep monitoring it in production. Microsoft’s RAG design guidance and Google’s grounding overview describe RAG as a way to provide relevant external or proprietary information to a model, not as a guarantee that its answers are true.
What RAG can—and cannot—do about hallucinations
A RAG system searches a selected body of information, supplies relevant passages to a language model, and asks the model to answer using that context. In an enterprise setting, the material might be internal policies, product documentation, or approved procedures. The intended benefit is evidence-based answers rather than relying only on what the model learned during training.
But the model can only use the evidence the system provides, and it may still misunderstand that evidence or make an unsupported inference. RAG can therefore lower the chance of unsupported output without eliminating errors. There is no suitable, attributable general statistic establishing a universal percentage by which RAG reduces enterprise hallucinations; results depend on the documents, retrieval setup, questions, and evaluation method.
Think of RAG as an evidence pipeline: source documents are prepared and indexed, a search retrieves passages for a question, the system assembles those passages as context, and the model generates an answer. Evaluation and production monitoring test whether each stage is working. Microsoft’s RAG design and evaluation guide treats these as connected design concerns.
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Build the evidence pipeline in stages
1. Curate authoritative, current sources
Decide which documents the assistant is allowed to rely on. Prefer authoritative material, and track who owns each source, when it was last updated, and which version is current. If a policy is superseded or two departments publish conflicting guidance, the system needs a way to identify that rather than treating every indexed passage as equally reliable. Source curation is a quality lever, but the vendor guidance does not prescribe one universal governance design; set rules that fit your organization and use case. See Google Cloud’s RAG overview.
2. Inspect document preparation and retrieval
Parsing, chunking, indexing, and search all affect what evidence reaches the model. A relevant answer may be absent from the results because a document was poorly extracted or split, the index is stale, or the search configuration did not find the right passage. Test these stages using representative questions from actual users. Check whether retrieved chunks contain the evidence needed to answer; do not assume a fluent final response means retrieval worked.
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Measure retrieval separately from answer generation. Keep a trace of the retrieved items so an investigator can distinguish a search failure from a model failure. Microsoft’s design guide covers document preparation, search strategy, and retrieval evaluation as distinct parts of RAG design.
3. Give the model explicit evidence rules
Tell the model to answer from the supplied context, to say when the context does not support an answer, and how to handle conflicting sources. Specify the expected answer format as well. Organize the context so the model can identify where each passage came from and what it applies to. These instructions help set behavior, but prompt wording alone does not make weak retrieval reliable; compare prompt changes through testing. See Microsoft’s RAG prompt engineering guidance.
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1Repair Windows errors before they cause bigger problems2Fix the driver behind crashes, sound loss and screen glitches3Clear out junk files and repair common Windows errorsA starting instruction might be: “Use only the evidence in the supplied passages. If it does not answer the question, say what information is missing. If sources conflict, identify the conflict and follow the organization’s source-priority rule. Do not infer a policy or fact that the passages do not establish.” Adapt the conflict rule and response format to the documents and task; this example is not a substitute for testing.
Evaluate retrieval and answers separately
A useful evaluation set includes representative questions, the evidence expected to support each answer, and reference answers where appropriate. Review retrieval first: did the system return relevant passages containing the needed facts? Then assess the final response. Microsoft recommends evaluating the end-to-end RAG response across multiple dimensions rather than relying on one score. Microsoft’s end-to-end evaluation guidance discusses these dimensions and experiment tracking.
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| Evaluation dimension | What it checks | Why it matters |
|---|---|---|
| Retrieval relevance | Whether the passages returned for a question are relevant and contain the evidence needed. | Shows whether an answer problem begins in search or context assembly. |
| Groundedness | Whether claims in the answer are supported by the supplied context. | Can expose claims that the retrieved material does not substantiate. |
| Correctness | Whether the answer is actually right, using appropriate reference material or expert review. | A response can cite or echo context yet still reach a wrong conclusion. |
| Completeness | Whether the answer covers the material parts of the question. | Finds omissions that a support check on individual claims may not catch. |
| Context utilization and relevance | Whether the response makes appropriate use of the available evidence and answers the question asked. | Helps identify answers that ignore useful context or drift off topic. |
Groundedness and correctness are not interchangeable. A response may be well supported by retrieved text but still interpret it incorrectly; a confident, plausible answer may also be unsupported. Use the dimensions together and preserve results and settings for both retrieval-level and end-to-end experiments. Microsoft’s evaluation guide and Databricks evaluation and monitoring guidance describe evaluation and monitoring practices.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Use grounding checks as one signal, not a truth guarantee
As one vendor-specific example, Google documents a grounding-check API that compares a candidate answer with reference facts, returns a support score and citations to supporting facts, and can use citation thresholds to filter answers likely to be ungrounded. Its documentation defines perfect grounding as every claim being supported by one or more facts. A grounding score does not establish that the facts are correct or that the answer’s reasoning is sound; validate thresholds and behavior on the workload before relying on them. See Google’s grounding-check documentation.
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Monitor the system after launch
RAG quality can change as documents, user questions, and the use case change. Retain enough information about inputs, outputs, and intermediate retrieval results to investigate failures, subject to your organization’s privacy, security, and retention requirements. Review newly observed questions and expert findings, then add relevant cases to the evaluation set and rerun evaluations when the corpus or system changes. The Microsoft Databricks monitoring guidance discusses logging and ongoing evaluation.
For workflows that could affect financial, legal, employment, or other high-impact decisions, do not treat a grounding score as a replacement for qualified human review. Make the review point explicit in the workflow and provide a way to escalate questions when the evidence is missing, ambiguous, or contradictory.
Compare RAG options against your workload
Vendor documentation shows different implementation approaches, but it does not establish a neutral winner or a comparative performance benchmark. Assess candidate architectures or hosted offerings against the conditions your organization actually faces:
- Evidence and corpus fit: Can the system connect to the authoritative sources your use case requires and keep them current?
- Retrieval controls and visibility: Can you inspect retrieved passages, diagnose search failures, and evaluate retrieval separately from generation?
- Access control and governance: Can the system respect your data permissions, ownership, and handling requirements?
- Operations: What work is required to prepare documents, maintain indexes, run evaluations, and monitor answers?
- Latency and cost: How do they behave under your actual query volume and workload?
These are evaluation criteria, not evidence-based rankings. Relevant architectural references include Google Cloud’s RAG infrastructure reference architecture and Microsoft’s RAG design guide.
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