Build Your Own LLM: The Complete Beginner-to-Expert Blueprint (2026) — Learn to Build, Train, Fine-Tune & Deploy Large Language Models

Build Your Own LLM is a complete, hands-on roadmap for going from zero to building, training, fine-tuning, and deploying real large language models — no prior AI background required.

Across 425 pages and 50 chapters, you'll move step by step from first principles (math, Python, neural networks) through the core architecture that powers modern AI (tokenization, embeddings, attention, transformers) into real hands-on builds (your own tokenizer, a mini transformer, a full training loop) and finally into professional-grade skills: fine-tuning open models like Llama, Mistral, Gemma, Qwen, and DeepSeek, building RAG pipelines and AI agents, and deploying production systems with Docker, cloud infrastructure, and GPU optimization.

Every chapter includes learning objectives, a hands-on coding project, a chapter summary, review questions, practical exercises, and a mini challenge — built for people who learn by doing.

What's inside

Part 1 — Foundations

• Ch 1: Welcome to the Age of AI
• Ch 2: History of Language Models
• Ch 3: Artificial Intelligence Fundamentals
• Ch 4: Mathematics for LLMs
• Ch 5: Python for AI Engineers
• Ch 6: Neural Networks
• Ch 7: Tokenization
• Ch 8: Embeddings
• Ch 9: Attention Mechanisms
• Ch 10: Transformer Architecture
• Ch 11: GPT Models

Part 2 — Build From Scratch

• Ch 12: Environment Setup
• Ch 13: PyTorch
• Ch 14: Build a Tokenizer
• Ch 15: Build a Mini Transformer
• Ch 16: Training Loop
• Ch 17: Train Your First Language Model
• Ch 18: Text Generation
• Ch 19: Evaluation
• Ch 20: Hugging Face

Part 3 — Working With Real Models

• Ch 21: Llama
• Ch 22: Mistral
• Ch 23: Gemma
• Ch 24: Qwen
• Ch 25: DeepSeek
• Ch 26: LoRA
• Ch 27: QLoRA
• Ch 28: PEFT
• Ch 29: Dataset Engineering
• Ch 30: ChatGPT-Style Assistant

Part 4 — Retrieval, Agents & Tools

• Ch 31: Retrieval-Augmented Generation (RAG)
• Ch 32: Vector Databases
• Ch 33: AI Agents
• Ch 34: Memory Systems
• Ch 35: Tool Calling
• Ch 36: Function Calling
• Ch 37: FastAPI
• Ch 38: Build a Modern AI Web App

Part 5 — Performance & Deployment

• Ch 39: GPU Optimization
• Ch 40: Quantization
• Ch 41: vLLM
• Ch 42: TensorRT
• Ch 43: ONNX Runtime
• Ch 44: Docker
• Ch 45: Cloud Deployment

Part 6 — Turning It Into a Business

• Ch 46: Build AI SaaS Products
• Ch 47: Selling AI APIs
• Ch 48: Building an AI Startup
• Ch 49: AI Security, Safety & Responsible AI
• Ch 50: The Future of Large Language Models
• Plus: Final Bonus Projects

Format

• 425 pages
• Digital download (PDF)
• Code-along projects in every chapter

Who it's for

Developers, students, and self-taught engineers who want a structured, project-based path into AI/LLM engineering — from someone who's never trained a model to someone who can build, fine-tune, and ship one.