Model Online / On-Site
Duration
Modules 6 modules

Course Objective & Scope

In interactive copilots, live support interfaces and voice AI systems, waiting several seconds for a complete response breaks the user experience. This course covers Server-Sent Events (SSE), WebSockets and WebRTC, async backend architectures, incremental JSON and tool-call parsing during streaming, and real-time voice architectures (Realtime APIs and STT–LLM–TTS pipelines).

Participants measure time-to-first-token (TTFT) and inter-token latency (ITL), and build a real-time service with user interruption (barge-in), stream cancellation, backpressure and streaming guardrail checks.

Who Should Attend?

  • Backend and full-stack engineers building real-time chat, copilots and voice assistants
  • AI engineers developing low-latency interactive AI services
  • Platform engineers managing high-concurrency connections and API gateways
  • Prerequisite: async programming experience in Python (asyncio/FastAPI) or Node.js/TypeScript

Key Takeaways

  • Select and implement SSE, WebSockets or WebRTC based on interaction requirements
  • Parse partial JSON payloads and streaming tool calls safely on the fly
  • Handle user cancellation, barge-in and disconnects without leaking compute or tokens
  • Design an end-to-end latency budget (VAD, STT, LLM, TTS) for real-time voice assistants
  • Run load tests measuring TTFT, ITL and concurrent stream capacity

Curriculum

Real-Time Communication Protocols for LLMs
  • SSE for unidirectional text, WebSockets for bidirectional state and WebRTC for media
  • Reverse proxy buffering, keep-alive and timeout configuration
Asynchronous Backends and Stream Control
  • Non-blocking stream pipelines in FastAPI/asyncio and TypeScript
  • Propagating client disconnects to cancel upstream LLM generation
Streaming Structured Outputs and Tool Calls
  • Incremental JSON parsing for progressive UI rendering
  • Assembling and executing chunked tool-call deltas during a stream
Real-Time Voice and Conversational Architectures
  • Modular STT–LLM–TTS pipelines versus native Realtime/Multimodal APIs
  • Voice Activity Detection (VAD), turn-taking and barge-in handling
Streaming Guardrails and Observability
  • Low-latency safety and PII checks on buffered token chunks
  • Tracking TTFT, time-per-output-token (TPOT) and aborted stream metrics
Lab: Building a Low-Latency Interactive Service
  • Developing a streaming text or voice service for an internal scenario
  • Latency profiling under concurrent user load

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