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Meeting it takes five measurements kept separate: retrieval quality, grounding, citation correctness, factuality and clinical safety. Recent reviews in JMIR (2026) and JAMIA (2025) both caution that retrieval augmentation alone does not guarantee any of them. The sections below set out how to build and run that test.
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Why a displayed citation proves so little
Citations are easy to render and hard to verify. A system can attach a real, working link to a sentence the linked page never states. It can also cite a document that supports a narrower claim than the one written. Treating “has a citation” as “is supported” collapses several different failure modes into one reassuring-looking feature.
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The five questions to keep separate
The JMIR scoping review treats these as distinct evaluation concerns rather than one “accuracy” score. Each one can fail while the others look fine.
| Layer | The question it answers | How it can fail while others pass |
|---|---|---|
| Retrieval quality | Did the system fetch relevant evidence for this query? Typical measures: context precision and retrieval recall, ideally against a reference evidence set. | The right guideline was never retrieved, so the model answers from weaker passages or from memory. |
| Grounding / faithfulness | Does the answer stay within the retrieved context? AWS Prescriptive Guidance defines faithfulness as: “Faithfulness – Assesses how accurately the generated response reflects the information in the retrieved context.” | The answer adds a plausible detail that appears nowhere in the retrieved text. |
| Citation / source correctness | Does the cited source exist, is it identified accurately, and does it support the specific claim it follows? | The URL resolves, but the page says something different or only partly related. |
| Factuality | Is the claim true against an external reference standard, including knowledge outside the retrieved context? | The claim is medically correct but ungrounded: nothing displayed to the user supports it. |
| End-to-end quality and safety | Is the answer relevant, complete enough, appropriately qualified, and safe for its declared context of use? | Every sentence is supported, yet a critical caveat or contraindication is omitted. |
The grounding-versus-factuality split matters most in practice. Grounding is about the evidence available at inference time. An answer can be right and still fail the claim-to-source contract, because the reader cannot verify it from what was shown. Report the two as separate scores rather than blending them. Sources for the definitions: JMIR 2026 and AWS Prescriptive Guidance (first published March 14, 2025).
Building the contract, step by step
1. Declare scope and source policy
State the task and audience the system is meant for. Then decide which source types are authoritative and in what order. Clinical guidelines, regulator material, primary research and local institutional policy are not interchangeable, and a retrieved snippet should not be treated as if they were. Record jurisdiction, publication or version date, and how often each source is expected to change. Source recency and geography belong to the claim’s context; they are not decorative metadata.
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2. Preserve provenance at retrieval time
Log what was actually retrieved, not just what was cited. AWS’s healthcare RAG guidance describes architectures that pass retrieved context into generation and recommends evaluating components separately. Capturing the evidence at retrieval time is what makes that separation possible. Without it, you cannot tell a retrieval miss from a generation error. A minimal record per response:
| Field | Purpose |
|---|---|
| Query and run context | Makes the run reproducible. |
| Corpus version | Lets you reassess outputs when guidance changes. |
| Stable document ID and passage ID | Lets a reviewer jump to the exact span. |
| Retrieved passage text | Freezes the evidence the model actually saw. |
| Source metadata (type, jurisdiction, date, version) | Supports authority and recency checks. |
| Claim-to-passage links | Ties each generated claim to its offered support. |
3. Verify at the claim level
Split each material answer into atomic claims, or at least into sentences, and link every one to the passage offered as its support. Judge each pair using a fixed label set:
- Direct support: the passage states or clearly entails the claim.
- Partial support: the passage supports part of the claim, or a weaker version of it.
- No support: the passage is related or unrelated but does not back the claim.
- Contradiction: the passage conflicts with the claim.
Put the judgment beside each claim, not in a summary at the end of the answer. Show the supporting passage itself or a direct path to it. A topically related citation scores “no support” if the specific assertion is not there.
4. Check scope and qualification
Many failures survive a simple “is it in the source?” check. For each supported claim, compare population, intervention, outcome, timeframe and degree of certainty against the source. A hypothetical illustration: if an answer says a treatment “reduces risk in adults with a condition,” but the cited passage reports an outcome in a narrower subgroup, or describes the evidence as preliminary, the claim has overreached its source. Mark it partial support. Preserve caveats and conflicts from the source in the answer; a broad statement should not be inferred from evidence that covers only a narrower population or outcome.
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Report retrieval metrics, claim support and citation correctness, answer relevance and completeness, and formal safety outcomes as separate lines. Then evaluate end to end. Use human review for clinically consequential claims, and document the rubric and the evaluators’ expertise. Automated or LLM-based judges can speed up the work, but until you have validated one against clinician-labelled examples, do not present its output as ground truth.
This layering is not the norm. The JAMIA 2025 systematic review found that only 4 of 16 studies (25%) included specific metrics for evaluating the retrieval process. Most reported measures focused on the final generated response, so a team that tests retrieval separately is already ahead of much of the published work.
6. Test the hard cases on purpose
A test set made only of well-covered questions will flatter the system. Build cases where the contract is under strain, and score whether the system abstains, qualifies its answer or routes to a human instead of producing confident text.
| Hard case | What a passing system does |
|---|---|
| Missing evidence (the corpus lacks the answer) | Says the evidence is not available; does not fill the gap from model memory. |
| Conflicting sources | Surfaces the disagreement and attributes each position; does not silently pick one. |
| Stale guidance (superseded version in the corpus) | Prefers the current version, or flags the date and version of what it used. |
| Ambiguous question | Asks for clarification or states its assumption. |
| Prompt that invites unsupported certainty | Keeps the source’s level of uncertainty; does not turn “may” into “will.” |
The JMIR review’s taxonomy highlights conflict handling and safety evaluation as important areas, so these cases are worth including in every release test.
7. Monitor source updates
Corpora change. Track a version for every collection, and when guidance is updated, re-run the claim checks on outputs that depended on the changed documents. Without corpus version logs from step 2, you cannot find those outputs.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What to report so results can be trusted
A support percentage means little without its design. Alongside any figure, state:
- the test set: how questions were chosen, how many, and which clinical domain;
- the verification unit: atomic claim, sentence or whole response;
- the reviewer method: who judged, with what expertise, against which written rubric, and whether an LLM judge was used and validated;
- the aggregation rule: how claim-level labels roll up into a response-level score, and whether partial support counts as pass or fail.
The Nature Communications gap between 75.7% statement-level and 38.4% response-level support is the reason the last two items matter. The same system reads very differently depending on how its labels are aggregated.
Comparing two systems or evaluation methods
When you compare healthcare RAG systems, or the methods used to evaluate them, line them up on the same seven axes rather than one overall score. These come from synthesizing the JMIR, JAMIA and AWS material.
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Repair common Windows errors and clear accumulated junk for a smoother, more stable PC - no reinstall needed.Free scan · no reinstall| # | Axis | What to look for |
|---|---|---|
| 1 | Retrieval | Recall, relevance and context precision, ideally against a reference evidence set. |
| 2 | Claim support and citations | Claim-level labels and citation correctness, not just link validity. |
| 3 | Source authority | Source type, date and jurisdiction. |
| 4 | Answer quality | Completeness and relevance to the question asked. |
| 5 | Uncertainty handling | Contradiction, uncertainty and abstention behavior. |
| 6 | Safety | Safety testing and fit for the stated clinical setting. |
| 7 | Evaluation design | Human expertise, rubric transparency, and whether any LLM judge was validated. |
How far the published numbers travel
Treat the figures in this area as context, not benchmarks. The JAMIA review found heterogeneous evaluation practices, with human evaluation, automated evaluation or both across its included studies. That limits simple comparisons of reported performance. Its overall odds ratios compare RAG with baseline LLM outcomes within that review, so they are not a universal effect estimate for RAG.
The JMIR review’s counts also describe what researchers wrote about, not what hospitals deploy. In its included records, clinical question answering was the most frequently represented application (89 of 157, 56.7%), followed by clinical decision support (70 of 157, 44.6%). They do indicate where claim-to-source checking is most needed: question answering is exactly the setting where users read a sentence and trust the citation beside it.
Finally, avoid two claims in your own documentation: that RAG prevents hallucination, and that a displayed citation demonstrates clinical reliability. Neither is supported by this evidence.
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