How to Catch Quality Regressions
Quality regressions are silent killers. Users notice before your metrics do. Automated regression detection catches drops before they become incidents.
Blog
Deep dives into LLM inference optimization. Practical insights for developers and founders building with AI.
When to Use LLM-as-Judge
LLM judges excel at subjective quality. They fail at factual correctness. Knowing when each applies determines whether your evals are useful or misleading.
Treating Evals as Non-Negotiable Constraints
If your optimization breaks an eval, the optimization is wrong. Evals are invariants, not suggestions. Ship nothing that fails them.
How the Big Labs Actually Do Evals
Evals at Anthropic, OpenAI, and Google aren't afterthoughts. They're gating functions that block releases. Every prompt change triggers the full suite.
Building Evals That Catch Real Problems
Bad evals give false confidence. Good evals predict production failures. The difference is designing for the problems users actually hit.
Evaluating Millions of LLM Responses
Human review doesn't scale. At 10M responses per day, you're sampling 0.001%. Automated evals are the only path to quality at scale.
What to Monitor in LLM Systems
Latency, errors, throughput, cost. The four numbers that tell you if your LLM system is healthy or heading for an incident.
Degrading Gracefully Under Load
When demand exceeds capacity, you have three choices: crash, reject, or degrade. Graceful degradation keeps serving, just worse.
Using Rate Limits to Control Spend
One runaway bug can burn $50K in a weekend. Rate limits aren't just for abuse prevention. They're your circuit breaker.
Starting Cheap and Escalating When Needed
Try the small model first. If it fails or isn't confident, try the large one. Cascade routing gets 80% savings on 80% of requests.