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 RAG answer that takes four seconds could be slow retrieval, a fat prompt, or the model itself. Nested traces tell you which one — here's how to find the bottleneck.
Read morePass token usage on every generation and Currai turns it into cost — rolled up per trace, model, user, and day. Stop guessing what an LLM feature costs.
Read moreOne conversation is many traces. Pass a session id to stitch every turn into one thread, and a user id to slice cost, latency, and volume by the people using your app.
Read moreTrace, span, generation — three nouns that cover everything an LLM app does. Understand the data model and your instrumentation stops being guesswork.
Read morePer-trace pricing punishes you for instrumenting well. We price on the data Currai actually processes, so big traces and tiny traces cost what they are.
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