RAG for Voice Agents

Knowledge retrieval fast enough for conversation

Build retrieval-augmented generation for voice agents where latency is the constraint. Learn voice-optimized retrieval patterns, context injection timing, MCP tool integration, citation in spoken conversation, and production monitoring.

What You Build

Voice agent with RAG that retrieves and cites knowledge within a 400ms latency budget.

Prerequisites

→Course 1.1
→Course 2.1

Chapters

RAG architecture for voice

25m

Why voice RAG is different from chat RAG: the 400ms latency budget, when to retrieve (on_user_turn_completed hook), how to inject context, and when RAG is the right pattern versus tools or static instructions.

Latency budgeton_user_turn_completedContext injectionRAG vs tools vs instructions

MCP tools for structured data

20m

Use MCP (Model Context Protocol) to connect agents to external APIs and databases. Combine MCP tools for structured data with RAG for unstructured knowledge.

Model Context ProtocolTool serversRAG + MCP combinationStructured vs unstructured data

Citation in spoken conversation

20m

Cite sources naturally in speech — 'According to section 3 of your policy...' not '[1]'. Track source metadata through the pipeline and build audit trails.

Voice-friendly citationsSource trackingHallucination preventionAudit trails

Production RAG: caching, monitoring, evaluation

20m

Optimize production RAG with semantic caching for frequent queries, monitor retrieval quality (relevance scores, hit rates, latency), and evaluate answer accuracy with LLM-as-judge.

Semantic cachingRetrieval monitoringAnswer quality evaluationCache invalidation

What You Walk Away With

RAG architecture optimized for voice AI: fast retrieval, proper context injection timing, natural spoken citations, MCP tools for structured data, and production monitoring.