Lecture 03 starts from the project question and works through RAG's motivation, chunk boundaries, approximate vector search, BM25 / dense / hybrid / rerankers, and letting the agent decide when to retrieve. The assigned Lewis et al. RAG paper and ColBERT add the original recipe and late interaction.
Lecture 08 first asks whether a single agent is enough, then compares orchestrator, swarm and hybrid patterns, communication through handoffs, message boards and A2A, and coordination infrastructure such as task ledgers, shared memory and worktree isolation, before tracing why multi-agent systems fail. The readings run from AutoGen to MAST, the single-agent counterargument and DyLAN.
Lecture 11 lays out the forms and sources of agent data: traces, demonstrations and feedback, data for optimization versus evaluation, data flywheels, synthetic data and data from human-agent interaction, with a warning that data can teach models unintended shortcuts. The readings are Shankar's data flywheel essay and a survey of LLMs for data annotation and synthesis.
Start with the opening slides on the agent loop, components, and engineering challenges, then examine BAIR's compound systems, Ng's work patterns, and Si's execution-feedback experiments. Distinguish the lecture, required reading, and additional readings.