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.
A step-by-step plan to implement customer service automation: pick the right processes, prepare knowledge, roll out in stages, and measure accuracy.
Read moreCompare the best customer service automation tools in 2026 by category — help desks, AI agents, chatbot builders, and observability — and how to choose.
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 moreThe 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 moreHow leading companies across industries use AI chatbots for customer service, the patterns behind successful deployments, and what to learn from them.
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