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.
New to evaluating LLM apps? Start here. What evaluation is, why 'it looks good' isn't a strategy, and the smallest useful loop you can stand up this week — no framework, no jargon.
Read moreAn honest look at AI customer support in 2026 — what genuinely works, why many deployments fail, and how to build one that customers actually trust.
Read moreHow AI delivers multilingual customer support across dozens of languages, what breaks in translation, and how to keep answers accurate in every language.
Read moreAn agent can call the right tool and still fail the task — or complete the task despite a clumsy path. Here's how to score tool-calling and task completion as separate metrics, so a failure points to a fix.
Read moreA step-by-step build for a chatbot that answers questions from your documents with RAG — plus the part most tutorials skip: how to know its answers are grounded and correct instead of confidently wrong.
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