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.
Production traces carry prompts full of user data and arrive at full traffic volume. Here's how to sample for cost and redact for privacy without losing the traces you need.
Read moreA single runaway prompt or retry loop can 10x your bill overnight. Here's how to turn the cost data on your traces into budgets and alerts that warn you before the invoice does.
Read moreYour LLM calls don't all live in Python. Currai ingests traces over plain HTTP and OTLP, so any language that can make a request can send traces — here's how.
Read moreA chatbot that works for one message often breaks by message seven. Here's how to trace a full conversation — sessions, history, and cost — so you can debug the whole thread.
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