Large Language Models From Zero to AI Engineer

About this PDF

Stop just calling APIs—learn how to architect, train, and ship real-world Large Language Models from the ground up. Large Language Models (LLMs): From Zero to AI Engineer is a 103-page, hands-on engineering blueprint that takes you inside the black box of modern Generative AI. Built for Python developers, CS students, and aspiring AI engineers, this guide bridges the gap between intuitive explanations, exact Transformer mathematics, and production-ready code: Demystify the Internals: Go from linear algebra, backpropagation, and Byte Pair Encoding (BPE) tokenization to building Causal Self-Attention, Grouped-Query Attention (GQA), RoPE, and SwiGLU layers in Python. Master the Modern AI Stack: Learn how frontier models (GPT, Claude, Gemini, Llama, and Qwen) are pretrained, aligned with DPO, and fine-tuned on a single GPU using 4-bit QLoRA. Build Production RAG & Autonomous Agents: Design hybrid vector + keyword retrieval pipelines with FAISS and ChromaDB, Cross-Encoder rerankers, and tool-calling ReAct agents that reason and act safely. Optimize, Serve & Ship: Crush latency and VRAM bottlenecks with INT4 quantization, KV caching, FlashAttention, local Ollama models, high-throughput vLLM clusters, and streaming FastAPI microservices. Ship 8 Portfolio-Ready Projects & Ace the Interview: Includes 8 end-to-end project codebases, 17 custom architecture diagrams, 34 technical interview Q&As, and a step-by-step 90-Day AI Engineer Roadmap.

Topics covered

Headings from inside this PDF (103 pages in total).

1. Chapter 5: Neural Network Architecture — Perceptrons, MLPs, Embeddin…
2. Chapter 11: Retrieval-Augmented Generation (RAG) — Chunking, Embeddi…
3. Chapter 12: LLM Application Development — OpenAI/Gemini/Anthropic AP…
4. CHAPTER 01
5. Chapter 1: Introduction to Large Language Models
6. CHAPTER 02
7. Chapter 2: Mathematical Foundations

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PDF · 103 pages

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103 pages

Language

English

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2.5 MB

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Ashish

Published

8 November 2026