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10 chapters — concepts, definitions, diagrams, and PM mental models. Study in order or jump to what your job needs next.
Tokens, context windows, inference vs training, hallucination vs grounding, embeddings, RAG vs fine-tuning.
Prompt anatomy, role constraints, few-shot, chain-of-thought, self-critique, evals, function calling.
SQL as source of truth, text-to-SQL pipeline, threat model, safety, validation patterns.
Hypothesis-driven charts, chart type selection, Python plotting, quantitative proof.
Full retrieval pipeline: chunking, embeddings, vector search, reranking, generation with citations.
Multi-turn context management, streaming UX, logging, rate limits, incident playbook.
Reproducible demos, pinning prompts, token cost budgeting, demo script structure.
Agent loops, tool calling, human gates, multi-step evals, production launch checklist.
Real interview questions for AI PMs and TPMs — foundations, product design, metrics, technical depth, strategy, and behavioral stories. Expandable answers with frameworks.