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A free scan shows the junk files, broken settings and background clutter dragging Windows down - then fixes them in one click.Free scan · Windows 10 & 11Retrieval-augmented generation (RAG) can reduce unsupported answers by giving a language model relevant enterprise evidence to use. It cannot guarantee correctness: the system may retrieve the wrong material, miss a key source, or draw an invalid conclusion from evidence it did retrieve.
To make RAG more dependable, treat it as an evidence pipeline—not just a prompt or a search box. Curate the information it can access, test retrieval separately from answer generation, evaluate answers for both support and correctness, and monitor failures after deployment.
What RAG can—and cannot—do about hallucinations
A RAG system searches an external or enterprise knowledge source for material relevant to a question, then supplies that material to a language model as context for its answer. This can anchor responses in documents the model would not otherwise have available, including proprietary or organization-specific information. Microsoft’s RAG design guidance treats the solution as a set of connected design and evaluation decisions rather than a single model setting.
Every stage can affect the result: source quality, document parsing and chunking, indexing and retrieval, context assembly, generation, and evaluation. If a needed policy is stale or absent from the corpus, retrieval cannot supply it. If search returns a loosely related passage, a fluent answer may still be unsupported. And even when the retrieved passage is relevant, the model may misread it or infer more than it says. Google likewise describes grounding as a way to base responses on retrieved information, not as a guarantee that every answer is correct: Ground responses using RAG.
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There is no responsible universal percentage to promise for how much RAG reduces enterprise hallucinations. The effect depends on the use case, corpus, retrieval setup, and evaluation method; assess it on your own representative questions rather than relying on a headline number.
How to build a RAG system that is easier to trust
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Curate the sources before indexing them
Choose material that is authoritative for the questions employees will ask. Track who owns each source, how current it is, which version applies, and who is allowed to see it. These are practical governance choices, not a universal design prescribed by vendor guidance; the controls should reflect your organization’s data and access requirements. Google’s RAG overview explains the role of external data in RAG, while Microsoft’s design guide covers preparation and evaluation as parts of the solution.
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Test whether retrieval finds the right evidence
Use representative real questions, including cases where the correct response depends on a specific document or version. Inspect the passages returned for each question: do they contain evidence that actually answers it, or merely share keywords? Test parsing, chunk size and boundaries, indexing, search strategy, and retrieval settings against those questions. Keep retrieved items in diagnostic traces so a failure can be attributed to search or to answer generation instead of guessing. Microsoft separates document preparation, search strategy, and retrieval evaluation in its RAG solution design guidance.
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Tell the model how to handle evidence
In the prompt, direct the model to answer using the supplied context, acknowledge when it lacks enough evidence, and follow a defined rule when sources conflict—for example, which authoritative source or version takes precedence. Specify the expected answer format and organize the context so passages and their sources are distinguishable. These instructions help set behavior but do not fix poor retrieval; test prompt changes with the same representative questions. See Microsoft’s prompt engineering guidance for RAG.
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Evaluate the answer on more than one dimension
Build a test set with expected evidence and, when appropriate, reference answers. Measure retrieval quality separately from end-to-end answer quality. For each response, consider:
- Groundedness: Is each material claim supported by the supplied context?
- Correctness: Is the answer actually right, including whether its interpretation or inference is valid?
- Completeness: Does it include the important information needed to answer the question?
- Relevance and context use: Does it address the question and make appropriate use of the retrieved material?
A grounded answer can still be wrong if it reasons incorrectly from a passage; correctness and groundedness are not interchangeable. Microsoft’s end-to-end evaluation guidance discusses evaluating responses across multiple dimensions and recording experiment settings and results.
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Keep useful traces and review failures
In production, retain enough information about inputs, outputs, and intermediate retrieval results to investigate where an answer went wrong, subject to your organization’s privacy, security, and retention requirements. Review expert feedback and newly observed questions, then add suitable cases to the evaluation set. Repeat evaluations when the corpus, user questions, or use case changes. Microsoft’s evaluation and monitoring guidance covers tracing and ongoing assessment of RAG applications.
How to investigate an unreliable answer
Trace the answer backward through the pipeline instead of immediately changing the model or prompt. A practical diagnosis follows the system’s stages:
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- The answer uses outdated or contradictory facts: inspect source ownership, freshness, and versioning; confirm which documents were eligible for retrieval.
- The answer omits a relevant fact: check whether the source was parsed and indexed, then whether retrieval surfaced the passage for that question.
- The retrieved passages are irrelevant: review chunking, indexing, and search choices using representative queries before judging generation quality.
- The context supports the topic but not the claim: inspect how the model interpreted the passages, and make missing-evidence and conflict handling explicit in the prompt.
- The claim is supported but the conclusion is wrong: treat it as a reasoning or correctness failure; a grounding score alone will not catch every invalid inference.
Recording both retrieval results and final responses makes these distinctions possible. Evaluation and monitoring guidance from Microsoft Databricks describes using intermediate results to diagnose application behavior.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.When to use automated grounding checks
Grounding checks can add a screening step, but they should not be treated as proof that a response is true. Google documents an API that compares a candidate answer with reference facts, returns a support score and citations to supporting facts, and can filter answers using a citation threshold. Its definition of perfect grounding requires every claim to be supported by one or more facts. This is a Google-specific implementation example, not a universal benchmark; validate its behavior and any threshold on your own workload before using it to block or approve answers. See Google’s grounding-check documentation.
How to assess a hosted RAG service or architecture
There is no neutral winner established by the vendor documentation reviewed. Compare candidate systems against your workload and requirements rather than treating a vendor example as a head-to-head result. Useful evaluation axes include:
- Whether the service can connect to the sources your organization relies on, and how well those sources can be prepared and kept current.
- What control you have over retrieval and whether you can inspect retrieved evidence and evaluation results.
- Whether access controls and data-governance requirements fit your environment.
- The operational work required to maintain the corpus, indexes, evaluation set, and monitoring.
- Latency and cost under your actual query volume and workload.
Microsoft’s RAG design guide and Google Cloud’s reference architecture illustrate vendor approaches. Use them to understand implementation patterns, not as independent comparative evidence.
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