All series08 volumes
Contents
Table of contents
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Showing 1-8 of 8
- Vol.01LLM FoundationsTokenizer behavior, model families, embeddings, inference, context windows, and what model capabilities actually mean.0 chapters100+ planned
- Vol.02Context EngineeringPrompt systems, structured context, memory, tool instructions, compression, and reliable multi-turn behavior.0 chapters120+ planned
- Vol.03RAG SystemsRetrieval architecture from chunking and ranking to citations, freshness, permissions, and production tuning.0 chapters150+ planned
- Vol.04Agents and ToolsTool calling, planning loops, MCP-style integrations, task decomposition, and controlled autonomy.0 chapters150+ planned
- Vol.05Evals and QualityGolden datasets, rubric grading, regression suites, adversarial tests, human review, and release gates.0 chapters120+ planned
- Vol.06AI Platform OpsLatency, cost, caching, routing, observability, tracing, batch workflows, and deployment patterns for AI apps.0 chapters120+ planned
- Vol.07AI SecurityPrompt injection, tool abuse, data leakage, model supply chain, policy controls, and safe fallback design.0 chapters100+ planned
- Vol.08AI Product SystemsChoosing AI use cases, designing human-in-the-loop flows, measuring value, and avoiding demo-only products.0 chapters140+ planned