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.
Currai is byte-compatible with the Langfuse SDKs. If you're already instrumented, you migrate by changing the host — your spans, exporters, and trace code keep working.
Read moreTotal latency hides the metric users actually feel — time to first token. Here's how to capture both on every generation and find what's making your LLM app feel slow.
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 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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