Reading Berkeley CS288 Spring 2026 from n-grams through RAG, reasoning, and agents using its 18 public slide units and three assignments.
CS288 moves from n-grams to RAG, reasoning, and agents through 18 public slide units and three assignments; Berkeley-only recordings make this an A3 materials route, not a public video course.
The first four units make text countable, representable, and classifiable; A1 then moves from n-grams and perceptrons to an NBOW MLP.
Units 05–07 move from recurrent state to encoder-decoder models, then rewrite the information path with attention and Transformer blocks.
Units 08–12 turn a base model into an interactive system: pre-training establishes capability, post-training shapes behavior, and generation plus evaluation determine how outputs are used.
Units 13–14 connect models to external knowledge; A3 requires data collection, QA annotation, indexing, and ablations under CPU and latency constraints.
Units 15–18 place NLP models inside perception, reasoning, tool, and environment loops; the question shifts from next-token prediction to allocating inference compute and validating multi-step action.