engineering · 1 min read
Continuous Improvement: The AI Quality Flywheel
AI quality compounds when you build the right flywheel: production samples → evals → prompt iteration → re-eval. Here is how to operationalise it.
BABhogar AI TeamProduct & Engineering
AI quality improves fastest when production samples flow into evals, evals drive prompt iteration, and re-eval validates the change. Most teams do this by hand; the use is in automating the loop.
Why it matters
The flywheel breaks down at handoffs: collecting samples, labelling them, promoting to evals, running iteration, validating. Automating the handoffs is what unlocks compounding gains.
How Bhogar AI approaches it
Bhogar AI ships a quality-improvement flywheel: production sampling, label queues, eval-set promotion, prompt experimentation and re-eval as a connected pipeline.
- Production sampling with stratification
- Label queues with reviewer assignment
- One-click eval-set promotion
- Prompt experimentation harness
- Re-eval automation on every change
What you get
Teams running the flywheel ship measurable quality improvements every quarter instead of every "rewrite".