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.
Per-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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