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.
Summarization looks easy to grade and isn't — a fluent summary can be unfaithful, incomplete, or subtly wrong. Here's how to score the two things that actually matter: faithfulness to the source and coverage of what mattered.
Read moreA plain-English guide for small business owners choosing an AI chatbot in 2026: what it can do, what to look for, what it costs, and how to start small.
Read moreActive observability turns production LLM traces into continuous signals for quality, cost, latency, prompts, tools, and evals before users report a problem.
Read moreA plain-language explanation of RAG — how retrieval grounds an LLM in your data, why it's the default architecture for factual AI apps, and the two places it quietly breaks that you have to evaluate.
Read moreWhat customer service automation is, which processes to automate, how AI changes it in 2026, and how to automate support without hurting quality.
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