Book 10 — Generative AI

Production systems, retrieval-augmented generation, and continuous diffusion architectures — from semantic embeddings and grammar-constrained decoding to hybrid vector search, GraphRAG, and latent diffusion models.

LLM Applications

• Embeddings
• Prompt engineering
• Structured outputs & function calling

Comprehensive Deep Dive:

• 📖 LLM Application Engineering Deep Dive

RAG

• Vector databases
• Chunking, retrieval, reranking
• Hybrid & semantic search
• Knowledge graphs
• Evaluation, hallucinations, grounding

Comprehensive Deep Dive:

• 📖 Enterprise RAG Systems Deep Dive

Diffusion

• Forward/reverse diffusion
• Denoising & U-Net
• Latent diffusion / Stable Diffusion concepts

Comprehensive Deep Dive:

• 📖 Diffusion Models Deep Dive

Mastery Roadmap

Each topic includes comprehensive mathematical derivations, architectural diagrams, runnable Python/PyTorch implementations from scratch, common debugging pitfalls, staff-level interview questions, and a 10-level mastery ladder.