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.
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.
Read moreA 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 moreInstall the SDK, paste your keys, wrap a call. A walkthrough of going from print() debugging to a real trace you can replay.
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.
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