How We Think About This
The value of this topic comes from repeatability, not novelty. Teams should be able to run it weekly with stable outcomes.
Where We Draw the Line
We repeatedly see teams celebrate offline gains and still lose user trust because evaluation sets lag real usage by just a few weeks. The practical fix is boring but effective: freeze one stable benchmark, then maintain a fast-moving shadow set that captures fresh failure patterns every sprint.
Start With Business Outcomes, Not Benchmark Scores
Evaluation should reflect the cost of failure in your product context. A support assistant and a coding copilot need different success definitions. If you optimize only for one aggregate score, you can overfit offline tests while user satisfaction declines.
Define what success and failure look like in user terms, then map those to measurable labels.
Offline Evaluation: Small, Clean, Representative Sets Win
Public benchmarks are useful but rarely match your production distribution. Build a domain-specific set that includes frequent intents, edge cases, and known failure patterns.
Track run metadata (model version, temperature, token limits, retrieval settings) so results stay comparable across releases.
Multi-Layer Metrics
Use multiple dimensions:
- task completion rate
- retry rate
- rework/edit cost
- safety false-positive / false-negative balance
If you must present one headline metric, choose the one most correlated with business impact.
Online Signals and Human Audits
After launch, track trends rather than single-day spikes. Combine telemetry with periodic human review of stratified samples to keep quality grounded in real use cases.
Include latency and cost in evaluation, not just quality. A “better” model that doubles cost may still be a net regression.
Human review does not need huge volume, but it must be continuous and methodical.
Reporting Standards
Document dataset provenance, sample size, model versions, and uncertainty. State limitations explicitly (sampling bias, noisy labels, narrow domain coverage).
When offline metrics improve but users complain more, revisit your metric design.
Keep the Evaluation Loop Maintainable
Store datasets and scripts together, automate report generation, and require new bad cases in every major release cycle.
Takeaway
Good evaluation is not about proving your model is “smart.” It is about helping teams find and fix failures faster with clear evidence.
A Better Review Rhythm
- Weekly: top regressions and unresolved risks.
- Biweekly: threshold adjustments based on real traffic evidence.
- Monthly: remove stale rules and archive low-value checks.