ThumbGate Verification evidence
guide | rag precision tuning guardrails

# RAG Precision Tuning Can Break Agentic Pipelines Quietly

Embedding fine-tunes and threshold tweaks can improve one precision metric while degrading broad retrieval recall. ThumbGate gates retrieval changes with baselines, verifier checks, and latency budgets before agentic RAG output triggers downstream actions.

👍 Thumbs up reinforces good behavior
👎 Thumbs down blocks repeated mistakes

## Why this page exists

- A precision win is not safe unless recall@k, precision@k, answer-with-evidence, and latency are compared against a saved baseline.
- Agentic RAG raises the risk because one retrieval miss can cascade into tool calls, decisions, or workflow changes.
- ThumbGate now exposes npx thumbgate rag-precision-guardrails for retrieval-tuning and verifier rollout checks.

## Why this became urgent

Recent retrieval research surfaced a failure mode that matches ThumbGate perfectly: a system can look better on one tuning objective while quietly getting worse at the general retrieval job the agent depends on.

That is not only an answer-quality problem. In an agentic pipeline, retrieved context can determine which files get edited, which customer gets contacted, or which operational action runs next.

## High-ROI gates to enable

- Require a retrieval baseline before embedding fine-tunes, threshold changes, or top-k changes.
- Block rollout when recall drops without a rollback plan, even if a narrow precision metric improves.
- Require a second-stage verifier or reranker for structural near misses such as negation flips and role reversals.
- Checkpoint latency and precision tradeoffs before verifier stages become production dependencies.
- CLI path: npx thumbgate rag-precision-guardrails --baseline-recall=0.86 --new-recall=0.72 --threshold-change --agentic --structural-near-misses --json.

## Where this creates revenue

This is a sharp enterprise wedge for teams that already bought a vector database or RAG platform and now need governance. ThumbGate sells the missing safety lane: baseline proof, action gates, and retrieval-change review before autonomous agents depend on the new index.

## FAQ

Does higher retrieval precision always help RAG?

No. Precision tuning can improve a narrow objective while hurting broad recall or generalization. ThumbGate treats retrieval tuning as a gated change, not a harmless config tweak.

When do I need a two-stage verifier?

Use one when the workflow is sensitive to structural near misses, such as negation, role reversal, legal clauses, financial facts, policy exceptions, or anything that can trigger downstream agent actions.