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 step-by-step guide to building an AI FAQ chatbot on top of your existing docs, from ingestion and retrieval to evaluation and safe rollout.
Read moreA passing eval score is a false sense of security when the eval set is narrow and static. Here's why agents that ace offline evals still fail in production — and the fix.
Read moreThe OWASP Top 10 for LLM applications, translated from a checklist into detectable behaviors — what each risk looks like in a real trace, and how to catch it before it becomes an incident.
Read moreA step-by-step guide to building a website customer support chatbot that answers from your knowledge base, escalates cleanly, and is safe to launch.
Read moreEven a well-evaluated AI system fails in production through distribution shift, integration and compounding errors, and unmeasured dimensions. Here's how to catch each.
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