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.
AI evals are not just engineering tests. Product managers need real traces, domain judgment, and a repeatable loop for turning model failures into product improvements.
Hallucination evals work when they score the right failure mode. Currai ties groundedness, factuality, consistency, and review back to the production traces that created the answer.
The fastest useful LLM observability setup is one trace, one generation, and one flush. Currai gets you there without running collectors or rebuilding your app.
Browse implementation notes, observability guides, product decisions, and workflow ideas by topic.
Logs 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 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.
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