Blog
Highlights from the Currai blog: the posts worth reading first.
A complete, code-first guide to turning OpenAI Realtime voice conversations and text-to-speech generations into readable Currai User Stories and correctly nested traces.
A step-by-step guide to importing completed Retell AI calls into Currai with signed call-ended webhooks—without adding capture code to the live voice agent.
A step-by-step guide to importing completed Vapi calls into Currai with an authenticated webhook—without adding capture code or sharing a Vapi private API key.
Browse implementation notes, observability guides, product decisions, and workflow ideas by topic.
AI evals are not just engineering tests. Product managers need real traces, domain judgment, and a repeatable loop for turning model failures into product improvements.
Read moreSummarization 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.
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