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.
The 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.
Read moreGuardrails are the runtime checks that sit between your model and the world — catching leakage, injection, and unsafe output in real time. Here's what to guard, where guards go, and why guardrails and evals need each other.
Read moreA practical end-to-end playbook for LLM evaluation in 2026 — from defining quality and building datasets to choosing metrics, running evals, and closing the loop.
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