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.
A practical 2026 guide to adding an AI chatbot to your website: what to look for, how to set it up, and how to make sure it gives accurate answers.
Read moreA practical workflow for moving from real AI failures to evals, prompt improvements, and measured quality gains in Currai.
Read moreProduction traces explain what happened. Evals decide whether it was good enough. Currai keeps both together so AI teams can improve faster.
Read moreAgent quality is not only task success. Currai helps teams evaluate cost-efficiency across model calls, tokens, latency, tool usage, and outcomes.
Read moreCurrai helps AI teams replace subjective launch debates with traces, eval scores, prompt versions, cost, latency, and clear quality evidence.
Read more