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.
How to evaluate multi-turn LLM conversations in 2026 — measuring context retention, goal completion, and turn-level quality that single-response evals miss.
Read moreHow to run a structured red team against your LLM app — building attack cases, probing each vulnerability, scoring the results, and turning the whole exercise into a test suite you run forever.
Read moreBuild a lead qualification chatbot that asks the right questions, scores prospects, syncs to your CRM, and hands hot leads to sales around the clock.
Read moreWhy public benchmarks tell you almost nothing about your app, which metrics actually predict production quality, and the evaluation practices that separate teams who ship confidently from teams who guess.
Read moreBuild an appointment booking chatbot that checks real availability, books into your calendar, sends reminders, and cuts no-shows — step by step.
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