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To tell whether your RAG retrieval is broken, measure what documents it returns separately from how the model uses them. Check retrieved evidence against human relevance judgments when you have them; then evaluate whether the answer is grounded in that evidence, relevant to the question, and complete. Trace representative failures from query to retrieved chunks to final response. No single score or pass threshold can certify a RAG system as reliable.
What “broken retrieval” means
Retrieval is the upstream step: given a query, did the system find useful evidence and rank it so the model could use it? A weak answer can result from retrieval missing the necessary source, retrieving irrelevant or noisy chunks, or finding good evidence that the model then misuses or ignores. Those are different failures and need different checks.
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Microsoft Foundry distinguishes evaluating the retrieval process from evaluating the complete system. Its guidance describes labeled document retrieval as the precise route for debugging search quality when query relevance labels are available: Microsoft Foundry RAG evaluators.
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Build an evaluation set that reflects real queries
Start with realistic questions and the evidence each should retrieve. Include routine queries as well as difficult forms: simple, complex, multi-part, and misspelled questions. Google Cloud recommends varied “golden” questions and iterative baseline runs; refresh your set as user behavior and application requirements change: Google Cloud’s RAG evaluation guidance.
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- Record the expected relevant documents or passages for queries where you can make reliable judgments.
- Include cases where no relevant evidence exists, if the application is expected to recognize that situation.
- Keep examples of known failure modes, such as a missing source, irrelevant chunks crowding out useful ones, or an answer omitting a required fact.
Use the same set for each comparison. A small but carefully judged set can expose regressions, but it should not be treated as proof that every query type or user need is covered.
Measure retrieval directly when relevance labels exist
For each labeled query, compare the retrieved documents with the documents judged relevant. Inspect not only whether useful evidence appears, but also where it appears in the ranked results and whether it falls within the amount of context passed to the model. Microsoft Foundry documents retrieval measures including Fidelity, NDCG, XDCG, Max Relevance, and Holes.
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- NDCG: helps assess ranking quality, including whether more relevant results appear higher in the list.
- Holes: signals missing relevance judgments. A gap in labels can make an evaluation set look like retrieval failed—or hide a failure—because the ground truth is incomplete.
- Other listed measures: Fidelity, XDCG, and Max Relevance are additional measures in Microsoft’s labeled retrieval evaluator. Choose and interpret them according to the evaluator’s documentation and the behavior you need to diagnose.
These metrics answer questions about retrieval against the labels you supplied; they do not establish that those labels are exhaustive or that the final answer is useful.
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If you do not have human relevance labels, an LLM-judged context or retrieval relevance score can be a practical first diagnostic: it asks whether retrieved text appears useful for the query. It is not equivalent to checking results against human-labeled relevant documents. The score depends on the evaluator, so inspect examples and add human judgments where the consequences of a wrong decision justify the work.
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Some RAG evaluation metrics use LLM calls as part of their evaluation. Ragas, for example, lists faithfulness among its RAG metrics: Ragas metric reference. Treat model-judged outputs as an additional signal, not ground truth.
Evaluate the generated answer as a separate stage
Retrieval quality alone cannot show whether the model answered well. Score response behavior independently so you can tell whether the evidence was found but mishandled, or whether the answer is wrong because the needed evidence never arrived.
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- Groundedness: Are the answer’s claims supported by the retrieved context?
- Answer relevance: Does the response address the user’s question?
- Completeness: Does it include the expected critical information, or has it omitted something important?
Groundedness alone will not catch every omission: an answer can contain only supported statements and still leave out a necessary fact. Microsoft’s Azure architecture guidance recommends combining evaluation dimensions and notes that model responses are nondeterministic: Microsoft Azure RAG evaluation guidance.
Compare changes with a controlled evaluation loop
- Define the failure you want to prevent. Specify whether the concern is missing evidence, irrelevant context, unsupported claims, an off-topic response, or missing expected information. Pick measures that correspond to those outcomes.
- Run a baseline on the same representative set. Save the queries, retrieved documents or chunks, final answers, and evaluator outputs. Logging intermediate retrieval results makes it possible to trace a bad response back through the pipeline; see Databricks evaluation and monitoring guidance.
- Change retrieval choices deliberately. Microsoft describes parameter sweeps over retrieval algorithms, top-k, and chunk sizes. Where practical, change one choice at a time so you can see what caused a result to move: Microsoft Foundry RAG evaluators.
- Compare retrieval and answer outcomes. Check whether useful evidence appeared, whether it was ranked high enough or fit within the context budget, whether irrelevant chunks added noise, and whether the resulting answer was grounded, relevant, and complete.
- Review examples, not just aggregates. Inspect improvements and regressions on individual questions, especially cases that matter most to users. Use human review to challenge automated judgments and refine labels or expected answers.
When configurations trade quality against latency, cost, or complexity, include those operational measures in the comparison if they matter to your application. Microsoft Databricks discusses quality, cost, and latency as evaluation concerns; a quality score alone does not settle whether a configuration is appropriate.
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Set thresholds for your workload, not by convention
There is no established universal acceptable threshold for recall@k, NDCG, faithfulness, or completeness in the material cited here. A defensible target depends on the application’s query mix, the cost of missing or misrepresenting evidence, and review of actual examples.
Microsoft Foundry’s listed evaluators return scores on a 1–5 range and document a default pass threshold of 3. That is a vendor evaluator implementation default, not a universal definition of good RAG or a published benchmark. Confirm current evaluator details in the Microsoft Foundry documentation before relying on them.
Microsoft Foundry summarizes the value of labeled retrieval this way: “Retrieval quality is a bottleneck for your RAG, and you have query relevance labels (ground truth) for precise search quality metrics for debugging and parameter optimization”. The statement is from Microsoft Foundry documentation, not an attributed individual: Microsoft Foundry RAG evaluators.
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