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.
Why AI agent evaluation still needs humans in 2026, where to put them in the loop, and how to combine human review with automated evals on production traces.
A practical field guide to LLM evaluation tools — what each category is good at, where they break down, and how to pick one that survives contact with production traffic.
The best AI observability tools in 2026 compared on evaluation depth, quality-aware alerting, drift detection, cost tracking, and the production-to-eval loop.
Browse implementation notes, observability guides, product decisions, and workflow ideas by topic.
Learn how Currai helps teams monitor prompts, traces, evaluations, A/B tests, token usage, and costs in AI apps built with Lovable.
Read moreStrategies for deploying enterprise AI chatbots securely in 2026 — data protection, access control, permission-aware retrieval, and audit — without slowing rollout.
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 moreCompare AI chatbots that integrate with Confluence in 2026 on ingestion, permission-aware retrieval, refresh, citations, and where they deploy.
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.
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