Retrieval Quality Drift
Retrieval Quality Drift is the slow decay of a RAG pipeline's answer accuracy as client documents, query patterns, and source formats change after launch.
By InnovaAI ResearchPublished Updated
What is Retrieval Quality Drift?
“Retrieval quality drift → silent client churn”
Retrieval Quality Drift is the slow decay of a RAG pipeline's answer accuracy as client documents, query patterns, and source formats change after launch. Unlike a model swap, which is visible, drift is invisible: the same query that returned a cited, correct answer in month one returns a plausible but wrong one in month six. For agencies, this matters because grounded, source-cited output is the deliverable clients pay a retainer for, and a single hallucinated citation in a compliance-sensitive workflow can end the relationship. The framework says: treat retrieval accuracy as a monitored metric, not a launch checkbox. Concretely, Perplexity's Photon retrieval engine cut p99 latency from 800ms to 65ms, showing how fast the retrieval layer itself is evolving; an agency that never re-benchmarks its pipeline against newer engines will keep paying for last year's accuracy. Build a quarterly eval harness that scores citation precision on a frozen client query set, then swap or re-tune the retrieval layer when scores fall below threshold.