Blog
Practical posts on tracing, evals, prompt changes, token cost, and the production habits that keep AI products explainable.
Highlights from the Currai blog: the posts worth reading first.
Why AI agent evaluation still needs humans in 2026, where to put them in the loop, and how to combine human review with automated evals on production traces.
A practical field guide to LLM evaluation tools — what each category is good at, where they break down, and how to pick one that survives contact with production traffic.
The best AI observability tools in 2026 compared on evaluation depth, quality-aware alerting, drift detection, cost tracking, and the production-to-eval loop.
Browse implementation notes, observability guides, product decisions, and workflow ideas by topic.
What the popular LLM benchmarks actually measure, why a high leaderboard score doesn't mean your app will be good, and how to read benchmarks as a starting point instead of an answer.
Read moreAI agent evals need more than a final answer score. Learn how to evaluate tool calls, transcripts, outcomes, regressions, non-determinism, and production traces.
Read moreThe best AI eval tools for CI/CD catch prompt, model, retrieval, and agent regressions before deploy. Compare Currai, Promptfoo, Braintrust, Langfuse, and Phoenix.
Read moreA practical 2026 guide to AI chatbot compliance: disclosure rules, data protection, sector regulations, and the controls that reduce legal risk.
Read moreTesting an LLM app isn't like testing normal software — outputs are non-deterministic and open-ended. Here are the testing methods that actually work: unit, regression, adversarial, and production, and how they fit together.
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