Most RAG quality problems are not "model problems". They are evidence problems. The system retrieves content that is too broad, too stale, or only loosely related, and the model fills gaps with plausible text. Evidence scoring is a way to quantify whether retrieved sources are strong enough to support an answer.
Why evidence scoring matters
RAG systems need a consistent decision: answer, ask a clarifying question, or abstain. Without evidence scoring, this decision becomes subjective and unstable. With evidence scoring, you can implement answerability gates and tune them over time (see answerability gates).
Score evidence on four dimensions
A practical evidence score can be built from four signals:
- Coverage. Do retrieved sources contain the key entities and constraints in the question?
- Specificity. Do sources include concrete policy text, numbers, dates, or procedures rather than vague guidance?
- Agreement. Do sources converge or contradict each other?
- Freshness. Are sources recent enough for time-sensitive questions (see freshness evaluation)?
These signals can be computed partly from retrieval metadata and partly from lightweight LLM checks that extract and compare facts.
Make evidence scoring retrieval-first
A common mistake is scoring the final answer. Score the evidence first. If evidence is weak, the best answer prompt will still hallucinate. Retrieval-first improvements include:
- Better ranking and relevance tuning (see ranking and relevance).
- Query orchestration to retrieve the right sub-questions (see query orchestration).
- Chunking that preserves meaning and includes the relevant sentence.
Link evidence scores to citation quality
Evidence scoring should correlate with citation trust. When evidence score is low, citations often become misattributed. Use citation audits as a feedback loop:
- Sample answers by evidence score bucket.
- Check whether cited sources support key claims.
- Fix systematic issues in retrieval and prompting.
See citation audits and structured citations.
Use evidence scores as an operational metric
Evidence score is an early warning signal. Track:
- Evidence score distribution over time.
- Evidence scores by content domain and tenant.
- Correlation with user complaints and escalations.
When evidence scores drop after an embedding or corpus change, treat it like drift (see embedding drift monitoring).
Validate changes with a benchmark harness
Evidence scoring is easiest to tune when you have repeatable tests. Add evidence scoring to your RAG benchmark harness:
- Compare evidence scores before and after retrieval changes.
- Gate releases on non-regression of evidence quality.
- Store results with version metadata.
See RAG benchmark harness for a practical approach.
When evidence becomes measurable, grounding becomes manageable. RAG quality improves fastest when you treat evidence as the product, not just the model output.