Your Go-To RAG Faithfulness Check
Retrieval-Augmented Generation systems can produce impressively fluent responses, but how can you be sure they're actually faithful to your source documents? This article breaks down practical strategies for testing RAG accuracy, backed by insights from experts who've tackled this challenge in production environments. Learn how sentence-level testing and adversarial datasets can expose hallucinations before they reach your users.
Adopt Sentence-Level Tests Plus Unanswerables
My go-to method is sentence level grounding checks plus an "unanswerable" test set. We measure: citation coverage, meaning what percentage of answer sentences are supported by at least one retrieved chunk, and entailment style faithfulness, where we verify the claim is actually backed by the cited text, not just "nearby." Practically, this is a small curated evaluation set (50 to 200 questions per domain) that includes trick questions and questions where the correct behavior is "I don't know."
One failure this caught early was a confident answer citing the right document but the wrong section because chunking and retrieval favored a similar looking policy paragraph. The fix was to tighten chunk boundaries, boost section headers in retrieval, and require quote level evidence for high risk answers. We now run this evaluation on every retrieval config change before shipping.
Build Adversarial Sets Catch Outdated Sources
Our primary application here is constructing a curated, adversarial test set for grounding. It's relatively easy to assess whether a model is 'faithful' using the naive (and commonly used) metrics, as a starting point, but its important not to stop there, as there are plenty of ways that a model can fail in subtle ways. We specifically build case where the retrieval context contains plausible, but incorrect grounding information. In one case we retrieved an archived copy of a policy document stating 'we retain employee data for seven years' when the actual current policy was three. Prompting with 'what is the employee data retention period' and the model dutifully and faithfully answered, 'seven years', and then cited the wrong source. A naive faithfulness score would call that a pass because it was closely matched to the retrieved text. Building this into our test cases forced us to put a subtler source-weighting, recency-ranking layer on our model's retrieval logic that we'd never have thought to touch pre-production.
Confirm Claims Via Exact Passages
Break the output into small, clear claims, and test each claim against the exact source passages. Confirm names, numbers, dates, and stated relations match the sources without stretching meaning. Mark each claim as supported, partially supported, or unsupported based on concrete overlap.
Compute a simple coverage score so the final answer reflects how much is truly backed. Flag any unsupported claim for revision or removal before release. Make per-claim verification the default gate for every RAG answer, and start tracking claim-level support today.
Demand Majority Support Across Independent Runs
Run several independent retrievals using different queries, seeds, or retrievers to reduce bias. Check each claim for support across these runs and count how many confirm it. Accept a claim only when a clear majority of runs return matching evidence. This crowds out errors from a single weak search.
When the runs disagree, mark the claim as uncertain and prompt for more search or a narrower question. Apply this consensus rule before finalizing the answer to improve trust. Add majority evidence checks to your RAG workflow today.
Enforce NLI Contradiction Checks Before Release
Use a natural language inference check to compare each claim with the sources and label the relation as support, conflict, or unsure. Treat conflicts as serious and remove or rewrite the claim until no conflict remains. When mixed signals appear, prefer the source that is more current or more authoritative and note the choice. Keep a log of conflict cases to improve prompts and retrieval settings over time.
This adds a clear, model-driven test for harmful contradictions. Turn contradiction checks into a standard stage of your pipeline. Set up NLI-based contradiction screening in your system now.
Compose Answers From Precise Quotes
Build answers from quotes first, then add short connecting words around them. Pull exact spans from sources with page, paragraph, or token offsets so anyone can find them fast. Keep key facts inside the quoted spans and keep paraphrase to the simplest glue needed.
Require that each sentence ties back to a cited span to prevent drift. This method turns audits into quick checks and lowers the chance of hidden errors. Adopt a quote-first style and anchor your next RAG answer to exact spans now.
Use Gap Penalties To Raise Coverage
Score answers by how much of the text is backed by citations, and penalize the parts that are not. Treat any sentence or clause without a source as a gap and subtract points for it. Give bigger penalties to risky facts like numbers, timelines, or medical claims.
Set a minimum score to pass, and send low-scoring answers back for more retrieval. This pressure nudges models to include solid citations and cut filler. Add gap-based penalties to your scoring and raise faithfulness today.