LLM Applications — Retrieval, Agents, Voice

Build and measure the parts of an LLM application yourself. Start with tokens and retrieval, attach tools with agents and an MCP server, and finally wire up a voice assistant that listens, finds the answer and speaks. Model internals and serving costs live in the LLM Models and Serving path.

코스

• LLM Engineering — From the tokeniser to RAG evaluation, from the ground up
• AI Agents — A Graph, Not a Model — Stop conditions, failure paths, traces. Where a demo and a product part ways
• The agent dropped my database — Write an MCP server with the standard library and guard it three ways
• Voice AI Agents — a pipeline that listens, looks things up and speaks — Wire listening through speaking in a single CPU pod and measure the latency