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.
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.
Read moreHallucination 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.
Read moreLearn how Currai helps teams monitor prompts, traces, evaluations, A/B tests, token usage, and costs in AI apps built with Lovable.
Read moreThe best prompt engineering tools connect prompt edits to traces, evals, versions, and production outcomes. Here is how to choose the right stack for shipping AI features.
Read moreOffline test sets go stale fast. Currai runs LLM-as-judge evals on real traced outputs, so you can compare prompt quality on live traffic.
Read more