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.
A one-word prompt change can shift cost, latency, and quality. A/B testing prompt versions shows which wording wins in production.
Read moreLLM observability captures every prompt, completion, token, and tool call so you can explain what your model did and debug it faster.
Read moreOpenTelemetry is the open standard for traces. Currai ingests OTLP spans, so you can use the collector and instrumentation you already run and still get LLM-aware views.
Read moreObservability data is high-volume and append-heavy — the classic case for running ClickHouse yourself. Here's the real trade-off between self-hosting and a managed backend.
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