Benchmark-driven development

Make benchmarks a first-class signal in your dev loop — every change runs against a benchmark, every result is on the dashboard.

Most teams treat benchmarks as a one-time activity at the start of a project. Pick a model, glance at MMLU, get on with it. Benchmark-driven development flips that — every meaningful change to your AI feature runs against the benchmarks that matter, and every result lives on a shared dashboard.

The shape of the work

1. Pick your benchmarks. Public benchmarks for general capability + your own private benchmark suite for task-specific quality.
2. Wire it into your dev loop. Local: pytest-style harness running a small slice. CI: full benchmark suite on every PR. Nightly: full suite + production-traffic slice.
3. Track the score over time. Stratix's evaluation history page shows the score curve per benchmark.
4. Block regressions in CI. A score that drops below baseline blocks the PR.
5. Report wins to the team. When a change improves a benchmark, share it.

Why it works on Stratix

• 52+ public benchmarks ready to use — you don't have to host the data
• Private benchmark suites — upload your own dataset and grading config
• Score history — every run is recorded; trends are visible
• CI gates — easily wired via the SDK or CLI
• Compare models — when a benchmark gets stronger, see if a different model is now a better fit

Tools you'll use

• Stratix Public — Benchmarks catalog
• Stratix Premium — Benchmarks
• SDK: client.evaluations.create()
• CLI: layerlens evaluate

Outcomes you should see

You'll know this is working when:

• Every PR touching AI runs a benchmark gate — non-negotiable, not opt-in.
• Your benchmark gating eval runs in <5 minutes — fast enough that no one wants to skip it.
• Score-over-time trends are visible to the whole team, not buried in one engineer's terminal.
• Net-new benchmarks reach 80% of your team within a week — adding signal is cheap and shared.

Anti-patterns

• Benchmark inflation. Adding 30 benchmarks because "more signal is better." Pick 3-5 that matter, run them often.
• Treating public scores as your scores. Public scores are a leading indicator. Your private benchmark on your data is the verdict.
• Skipping CI. If benchmarks don't gate merges, they're decoration.

Where to next

• Tutorial: Wire CI/CD quality gates
• Workflow: Evaluate
• Concept: Models and benchmarks
• Cookbook: CI/CD recipes