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The dependable way to improve enterprise RAG accuracy is to measure the pipeline in separate stages. Test whether retrieval returns the right evidence, whether the model stays within that evidence, whether it answers every important part of the request, and whether the evidence itself is correct. Keep a representative test set, compare controlled changes against a baseline, and expose claim-level citations so people can audit answers. No single percentage can describe accuracy for every RAG workload.
Define “accuracy” for the job your RAG system performs
A retrieval-augmented generation (RAG) application searches a source corpus, passes selected passages to a language model, and generates an answer from the user’s question plus that context. Accuracy therefore has more than one meaning. A support assistant may need to find the current policy, quote it faithfully, answer all parts of a multipart question, and identify when the policy says nothing. A research assistant may need broader coverage and explicit source links. A compliance workflow may require that every material claim be traceable to an approved record.
Write the acceptance criteria for the actual workflow before choosing metrics. At minimum, define success for four questions:
- Did retrieval find the right material? Relevant evidence must be present in the context window.
- Did the answer stay faithful to that material? Claims should be entailed by the supplied passages rather than invented.
- Did it cover the request? A response can be faithful but omit a required date, exception, or second sub-question.
- Is the result useful and factually sound? The answer must address the user’s intent and rely on an appropriate, correct source.
Microsoft’s guidance recommends choosing metrics that fit the workload instead of forcing every application into one universal threshold. Its end-to-end evaluation documentation also warns that “Language model responses are nondeterministic, which means that the same prompt to a language model often returns different results.” Treat scores as distributions and trends, and inspect examples, not as an immutable guarantee.
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Model accuracy as a pipeline, not a model property
Separate retrieval evaluation from response evaluation. Microsoft Foundry describes retrieval as a process evaluation and the final response as a system evaluation. That separation tells you where to spend engineering time: a missing passage is a search or corpus problem, while an unsupported statement about a retrieved passage is a generation, prompt, or context-assembly problem.
| Pipeline stage | Question to answer | Typical failure | Useful evidence |
|---|---|---|---|
| Corpus and preparation | Is the authoritative information present and usable? | Outdated files, duplicate policies, broken tables, or inaccessible records | Document inventory, versions, ownership, permissions |
| Chunking and enrichment | Can a relevant fact be retrieved as a coherent unit? | Chunks split definitions from exceptions or contain too much unrelated text | Chunk samples, metadata, overlap and heading structure |
| Embedding and indexing | Does the index represent the language users actually use? | Acronyms, product codes, or domain terms do not match the query | Embedding model, vocabulary tests, index freshness |
| Retrieval | Are the best passages ranked into the context? | Wrong documents, low recall, or excessive irrelevant context | Query-level relevance labels and retrieved ranks |
| Generation | Does the model answer from the supplied evidence? | Unsupported claims, missed sub-questions, or instruction drift | Answer, context, prompt, and evaluator judgments |
| Product experience | Can a user understand and verify the result? | Unclear citations, stale answers, or no abstention path | Displayed sources, timestamps, feedback, escalation logs |
End-to-end scores are important, but they can hide a trade-off. A prompt change may improve wording while retrieval recall falls. A larger retrieval depth may increase coverage while flooding the model with contradictory passages. Keep stage-level measurements alongside the final answer score.
Evaluate retrieval before asking whether generation is good
Start with representative user questions and a relevance ground truth whenever possible. For each question, record which documents or chunks a subject-matter reviewer considers relevant, then compare the retriever’s ranked results with that set. Microsoft notes that document-retrieval evaluation requires retrieval ground truth; without a judgment of what is relevant, a retrieval score cannot tell you whether a result is actually useful.
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- Sample real workload questions. Include common requests, long and short wording, multipart questions, domain terminology, and misspellings that occur in production.
- Attach expected evidence. Identify the authoritative document and, where practical, the exact passage or chunk that supports each question.
- Record ambiguity. Mark questions with multiple valid sources, time-sensitive answers, or an intentionally absent answer. An abstention case is a test case, not a failure of the corpus.
- Freeze a versioned set. Store the question, relevance labels, corpus version, and evaluator notes so that every experiment uses the same comparison point.
If relevant evidence never enters the context, generation cannot reliably compensate. For low retrieval completeness, inspect corpus coverage first, then chunk boundaries, metadata filters, embedding vocabulary and model, and retrieval depth. Microsoft specifically recommends checking whether relevant chunks were missed and whether chunking or embeddings prevent a match.
Inspect ranking, not only the top answer
Review the first several retrieved items for each test question. Look for the authoritative passage, contradictory versions, and near-duplicate chunks that consume the context budget. A useful diagnostic record contains the query, filters, index version, retrieved identifiers and ranks, similarity or ranking signals, and the final context sent to the model. This makes a “wrong answer” reproducible as a retrieval defect or a later-stage defect.
Measure the generated response with complementary checks
One metric cannot distinguish an answer that is well-supported but incomplete from one that is comprehensive but invented. Microsoft’s RAG evaluators cover groundedness, completeness, relevance, correctness, and utilization; use them as a diagnostic set rather than a leaderboard.
| Check | What it measures | What a weak result suggests |
|---|---|---|
| Groundedness (faithfulness) | Whether claims are supported by the retrieved context | Prompting, context assembly, model behavior, or unsupported synthesis |
| Completeness | Whether the response covers the material parts of the question | Missing chunks, insufficient retrieval depth, short answers, or poor question decomposition |
| Relevance | Whether the response addresses the user’s actual intent | Query interpretation, routing, prompt instructions, or irrelevant context |
| Correctness | Whether the evidence and resulting statements are factually sound | Stale or conflicting source material, extraction errors, or generation errors |
| Utilization | Whether the answer makes appropriate use of the supplied context | Ignored evidence, context overload, or a prompt that does not prioritize sources |
Groundedness is necessary but not sufficient. An answer can be completely supported by one retrieved paragraph and still omit a required exception. It can also be grounded in a stale or incorrect document. Evaluate source correctness and answer completeness separately.
Microsoft defines groundedness this way: “Groundedness, sometimes referred to as faithfulness, measures whether the response is based completely on the context.” Use that definition for the context supplied to the model; it does not certify that the corpus itself is current or authoritative.
Build an evaluation loop that survives model variability
Prepare a stable golden set before changing the system. Google Cloud recommends golden questions that represent intended use cases and incremental testing. A practical loop is:
- Establish a baseline. Save the corpus and index versions, chunking settings, embedding model, retriever configuration, prompt, generation model, evaluator configuration, latency, and cost measurements.
- Run the same questions. Capture retrieved context, answer text, citations, metric scores, and reviewer comments for every case.
- Change one meaningful component. For example, alter chunking, retrieval depth, a metadata filter, the prompt, or the model. Isolating changes makes attribution possible.
- Compare distributions and examples. Report ranges, averages, and failure counts by query type. Inspect cases near the decision boundary and every severe failure.
- Keep or reject the change explicitly. Store the winning configuration and its results; do not overwrite the baseline.
- Expand the set from production feedback. Add newly observed intents, terminology, stale-document cases, and adversarial or abstention questions, then rerun regression tests.
LLM-based evaluators can scale review, but they are judges with limitations, not oracles. Calibrate them against human-labeled examples, preserve the evaluated answer and context, and use human review for high-impact decisions. Because outputs vary between runs, repeat selected tests or use a fixed evaluation protocol and interpret changes as a range or trend.
Diagnose failures with a decision path
- No relevant passage retrieved: verify corpus coverage and permissions; inspect chunk boundaries and metadata; test domain vocabulary in the embedding model; adjust filters or retrieval depth; then rerun retrieval labels.
- Relevant passage retrieved but answer invents a claim: inspect the assembled context for truncation or conflicting passages; tighten instructions to answer only from evidence; require an explicit “not found” response; and test claim-level support.
- Answer is supported but misses a requirement: label each sub-question and expected fact; increase retrieval coverage or use query decomposition; instruct the model to address each labeled part; then check completeness.
- Answer cites a plausible but wrong policy: enforce document version, effective-date, and authority metadata; remove superseded records from default retrieval; and include source-correctness review.
- Scores improve while users complain: segment results by intent, language, access role, and answer length; inspect examples rather than relying on an aggregate; and check whether the evaluator’s rubric matches the product’s actual definition of success.
Keep a failure taxonomy in your evaluation store. “Wrong context,” “unsupported claim,” “omitted requirement,” “stale source,” and “irrelevant answer” lead to different fixes; one blended score does not.
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Make every important answer auditable
Preserve the path from each answer claim to the retrieved source identifier and passage. In the interface, make a citation open the exact supporting excerpt, show the document title and version or effective date, and distinguish quoted evidence from model-generated explanation. This lets a reviewer verify the claim without searching the entire corpus.
Best Value
Google Cloud’s grounding documentation describes a support score and citations to facts supporting claims. It states: “Perfect grounding requires that every claim in the answer candidate must be supported by one or more of the given facts.” That is a criterion for entailment by supplied facts, not proof that the facts are complete, current, or correct.
Google’s documented grounding-check API limits are implementation-specific and can change. The current documentation says a request accepts at most 200 facts, each up to 10,000 characters, and an answer candidate up to 4,096 tokens. These are product limits, not general limits on RAG design. See the Google Cloud grounding documentation when implementing that API.
Use vendor tools as examples, not as accuracy guarantees
Microsoft Foundry provides RAG evaluators for retrieval and response dimensions. Its documented evaluator inputs and definitions are described in Microsoft’s RAG evaluator guide. Azure Architecture Center’s end-to-end phase guidance covers test documents and queries, chunking and enrichment, embeddings, index creation, search strategy, nondeterminism, and metric investigation in its evaluation-phase documentation.
Google Cloud documents grounding checks, support scores, and claim citations in its grounding API guide. Its practical evaluation advice recommends golden questions and incremental experiments in this RAG evaluation guidance. Neither set of materials establishes a head-to-head accuracy winner. Compare platforms on the same workload, using the same labeled questions and acceptance criteria.
| Comparison axis | What to verify in a proof of concept |
|---|---|
| Retrieval evaluation | Can you provide relevance labels, inspect ranked results, and separate retrieval from generation? |
| Response evaluation | Can you score groundedness, completeness, relevance, utilization, and correctness for your task? |
| Evidence display | Can users open claim-level citations and source excerpts? |
| Model and process choice | Can the service fit your chosen model, prompt, evaluator, and human-review process? |
| Operations | Measure latency, throughput, privacy controls, access enforcement, data residency, and cost with your own workload; the cited documentation does not establish comparative outcomes for these factors. |
Set deployment gates that reflect risk
Define separate release gates for retrieval and answers. A high-impact workflow may require every critical question to retrieve an approved source, no unsupported claim in a sampled audit, visible citations, and a safe abstention when evidence is missing. A lower-risk internal search assistant may tolerate more variation but still need regression protection for common questions.
- Version the corpus, index, chunking, embedding, prompt, model, evaluator, and test set.
- Log the user question, access scope, retrieved identifiers, answer, citations, and evaluator results while respecting retention and privacy requirements.
- Test permission boundaries: a highly grounded answer is still unacceptable if it exposes a document the user cannot access.
- Monitor drift from new documents, changed terminology, altered metadata, and changes in user intent.
- Provide an escalation route and an explicit “insufficient evidence” behavior instead of encouraging confident guesses.
Accuracy is earned by evidence management and disciplined evaluation. When retrieval, grounding, completeness, relevance, correctness, and auditability are measured separately and then checked together, teams can improve a RAG application without pretending that it has one permanent accuracy number.
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