LLMs, GenAI, RAG, agents, evaluation · insight-1592

Tier regression gates by cost and cache model outputs keyed by their full input

Situation
The full LLM eval takes 40 minutes and real money per run, so engineers skip it, or it runs on every commit and the bill and queue grow.
Action
Run deterministic checks (schema, forbidden strings, tool allow-list) on every commit, a small judged smoke set on each pull request, and the full set nightly or before release; cache model responses keyed by prompt, parameters and model version so unchanged cases are not regenerated.
Reason
Cheap fast checks catch most breakages early, and the expensive checks run where their cost buys a decision.
Boundary
Teams iterating weekly or faster on prompts, retrieval settings or models.

Symptom or risk

Either regressions merge unchecked or the gate is bypassed because it is slow, flaky and expensive.

Very small eval sets can run in full on every change.

A cache key that omits any input (system prompt, retrieved context, tool results) serves stale answers.

A smoke set that never changes and is slowly overfit; rotate its items from the full set.