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.
AI chat projects turn uploaded PDFs, spreadsheets, documents, and code into a reusable knowledge base. Learn how RAG, project memory, instructions, and source citations keep every chat grounded.
Read moreHow startups should approach LLM evaluation in 2026 — the minimum viable eval setup, what to measure, and how to build a quality loop without a big team.
Read moreChatbots fail across a conversation, not in a single reply. Here are the metrics that actually catch chatbot failures — and why turn-level scoring alone always misses them.
Read moreA practical review of Intercom's Fin AI agent in 2026: how it works, its resolution-based pricing, where it fits, and when to consider alternatives.
Read moreHow to use a model to grade model outputs — writing rubrics that hold up, avoiding the biases that quietly skew scores, and validating the judge against humans so you can trust it at scale.
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