How to use Currai with OpenAI Realtime voice and TTS
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.
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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.
Use the Model Context Protocol to manage your website chatbot from Claude — update knowledge, review conversations, and onboard the agent conversationally.
Read more ›How comparison-based LLM evaluation (arena-as-a-judge) works — pairwise judging of two outputs, why it beats absolute scoring for subtle quality, and how to use it.
Read more ›G-Eval turns a plain-language quality criterion into a repeatable score by having the judge reason through steps first. Here's how it works, why it beats a bare 1–5 prompt, and where it still needs calibration.
Read more ›Build a recruitment chatbot that screens candidates, answers job questions, schedules interviews, and syncs to your ATS — fairly and around the clock.
Read more ›The core RAG evaluation metrics explained — answer relevancy, faithfulness, contextual precision, contextual recall, and contextual relevancy — and what each catches.
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