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.
G-Eval turns a plain-language quality criterion into a repeatable score by having the judge reason through steps first. Here's how it works, why it beats a bare 1–5 prompt, and where it still needs calibration.
Read moreBuild a recruitment chatbot that screens candidates, answers job questions, schedules interviews, and syncs to your ATS — fairly and around the clock.
Read moreThe core RAG evaluation metrics explained — answer relevancy, faithfulness, contextual precision, contextual recall, and contextual relevancy — and what each catches.
Read moreNot every eval needs a model to grade it. Here's how to decide between deterministic metrics you can trust blindly and LLM-as-a-judge scoring you have to calibrate — and why the best suites use both.
Read moreWhat a customer service chatbot actually is in 2026, how modern AI chatbots work, the main types, and how to choose one that improves support quality.
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