What does this article cover?

How to score RAG sources for trust and freshness so retrieval prefers authoritative, current documents and reduces contradictions.

Teams running RAG over large knowledge bases where duplicates and outdated documents reduce answer quality.

Source Quality Scoring for RAG: Trust Signals, Freshness and Ranking

In many enterprises, the biggest driver of RAG quality is not the model. It is source quality. If the knowledge base contains outdated pages, conflicting documents, and low-quality content with unclear ownership, retrieval will surface bad evidence and the model will confidently repeat it.

Source quality scoring is a practical way to improve reliability without changing the model: assign trust signals to sources and use them in retrieval and ranking.

Define trust signals that are operational

Useful signals are ones you can measure and govern:

Ownership. Named owner and review cadence (see content review workflows).
Freshness. Last updated and ingestion timestamps (see freshness architecture).
Authority. Canonical source systems and approved domains (see canonical sources).
Policy constraints. Whether the source is allowed for certain intents or audiences (see permissions).

Represent scores as metadata

Quality scoring should be encoded as metadata fields so retrieval can filter and rank deterministically (see metadata strategy). Avoid embedding "trust" into prompts - it is not auditable.

Apply scores in retrieval and ranking

There are three common applications:

Filtering. Exclude low-trust sources for high-risk workflows.
Boosting. Prefer high-authority sources when relevance is similar.
Conflict handling. If conflicting sources are retrieved, surface both and ask for clarification rather than blending them.

Use reranking and feedback loops to tune the trade-offs (see ranking and relevance).

Measure whether scoring helps

Evaluate improvements with layered metrics:

Golden query suite: expected sources appear in top results.
Grounding metrics: answers cite authoritative sources (see grounding).
User outcomes: fewer escalations and corrections (see usage analytics).

Keep these checks running continuously so trust scores do not silently drift (see synthetic monitoring).

When source quality is explicit, RAG becomes more predictable: fewer contradictions, fewer stale answers, and faster trust-building with users.