Course Objective & Scope
High latency in an LLM application is often caused not by the model alone, but by chaining query rewriting, vector retrieval, reranking, guardrails and multiple agent steps sequentially. This course teaches how to manage an end-to-end latency budget at the application and orchestration layer to cut both real and perceived response times.
Participants profile a sluggish multi-step RAG/agent pipeline using trace waterfalls, then apply async parallelisation, SSE/WebSocket streaming, compact structured outputs and P99 tail-latency hedging to bring response times down dramatically.
Who Should Attend?
- Software engineers building interactive LLM, voice assistant and agent applications
- AI and platform engineers accountable for application performance and SLA targets
- Solution architects designing real-time AI user experiences
- Prerequisite: Python (async programming basics) and prior LLM application development
Key Takeaways
- Decompose and budget end-to-end latency across pipeline steps using trace waterfalls
- Execute independent retrieval, guardrail and tool calls concurrently with
asyncio.gather - Shorten decode duration directly by minimising generated output tokens
- Improve perceived latency with SSE token streaming and intermediate progress events
- Cap P95/P99 tail-latency spikes using tuned timeouts, fallbacks and hedged requests
Curriculum
End-to-End Latency Anatomy and Budgeting
- Breakdown of network, guardrail, embedding, vector search, reranking, TTFT and decode time
- Target latency budgets for chat, autocomplete, voice assistants and background workflows
Orchestration Parallelisation and Speculative Execution
- Refactoring sequential chains into parallel DAG branches for multi-query retrieval and tool calls
- Running input guardrails and initial retrieval speculatively in parallel
Accelerating Decode through Prompt and Output Design
- Why generating one output token takes far longer than processing fifty input tokens
- Eliminating conversational filler, using compact JSON keys, short codes and stop sequences
Streaming UX and Perceived Performance
- Server-Sent Events (SSE), WebSockets and incremental partial-JSON parsing
- Streaming meaningful status events while agents execute tool calls
Controlling P95 and P99 Tail Latency
- Per-step timeouts, hedged requests and fast-model failover during provider slowdowns
- Load testing P50/P95/P99 latency under realistic concurrency with Locust/k6
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