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 Voiceflow review for 2026: what it does well for building conversational AI agents, its pricing model, its limits, and when to consider alternatives.
Read moreHow to add an AI chatbot to your documentation or GitBook in 2026 — native docs AI versus dedicated agents — with grounding, citations, and freshness.
Read moreLLM observability captures every prompt, completion, token, and tool call so you can explain what your model did and debug it faster.
Read moreLogs tell you a line ran. Traces tell you what the model saw, said, and cost across the whole request. Here's why LLM apps need tracing, not more print statements.
Read moreLLM apps fail in ways your APM never sees. We built Currai so you can watch every prompt, token, and tool call the way you already watch latency and errors.
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