engineering · 1 min read
Building an Eval Pipeline That Catches Regressions
A good eval pipeline tells you a prompt change is bad before users do. Here is how Bhogar AI ships continuous evals.
BABhogar AI TeamProduct & Engineering
A useful eval pipeline runs on every prompt and model change, compares to a golden dataset and blocks deploys when quality regresses. Without it, regressions ship to production weekly.
Why it matters
The hard part is the dataset: too small and it misses regressions; too large and runs are slow and expensive. Production datasets are versioned and grown from real production samples.
How Bhogar AI approaches it
Bhogar AI ships eval pipelines with versioned datasets, sample-from-production tooling, LLM-as-judge for subjective metrics, and a CI hook that blocks deploys on regression.
- Versioned eval datasets
- Sample-from-production curation tooling
- LLM-as-judge for subjective metrics
- CI hook: block deploy on regression
- Per-prompt, per-model dashboards
What you get
Teams running blocking evals catch 80%+ of quality regressions before user impact.