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.
Golden datasets get better when they come from real failures. Currai helps teams turn production traces and human review into durable eval cases.
Read moreMulti-turn support quality depends on context, policy accuracy, escalation, and consistency. Currai traces make those conversations eval-ready.
Read moreFine-tuning is easy to run and hard to judge. Here's how to evaluate a fine-tuned model against its base — catching the regressions fine-tuning quietly introduces and proving the gain is real, not vibes.
Read moreAI evals are not just engineering tests. Currai helps PMs use production traces, rubrics, and domain judgment to improve AI product quality.
Read moreAI evals work best when product, support, engineering, and domain experts share traces, rubrics, and quality decisions in Currai.
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