Building ChatGPT-Like Systems

Building ChatGPT-Like Systems
Architecture, Training, Alignment & Production Deployment of Large Language Models
Large Language Models like ChatGPT are not just AI models, they are full-scale distributed systems combining machine learning, infrastructure engineering, data pipelines, alignment strategies, and production-grade deployment.
Building ChatGPT-Like Systems is a practical, end-to-end engineering guide for developers, architects, and technical leaders who want to understand how modern LLMs are actually built, trained, optimized, aligned, and deployed in real-world systems .
This book goes far beyond prompts and APIs. It breaks down the entire LLM stack , from mathematical foundations and transformer internals to GPU cost engineering, RLHF, retrieval-augmented generation (RAG), AI agents, and large-scale inference serving.
What you'll learn
✔ How transformer-based LLMs really work, from self-attention to GPT architecture
✔ How large language models are trained from scratch at scale
✔ Why decoder-only transformers power systems like ChatGPT
✔ How to fine-tune models using SFT, LoRA, and parameter-efficient methods
✔ How RLHF aligns models with human preferences
✔ How to design prompt systems as architecture , not hacks
✔ How RAG systems overcome hallucinations and knowledge limits
✔ How AI agents plan, reason, call tools, and recover from failures
✔ How inference is optimized using KV caching, batching, and quantization
✔ How to deploy LLMs with FastAPI, Docker, Kubernetes, and autoscaling
✔ How to estimate GPU costs, memory usage, and inference pricing
✔ How to monitor, evaluate, and operate LLMs in production (LLMOps)
Who this book is forSoftware engineers building AI-powered productsML engineers and deep learning practitionersBackend & platform engineers moving into AI systemsAI startup founders and technical product leadersResearchers who want engineering realism , not theory alone
No hype. No magic. Just how ChatGPT-like systems actually work .
What makes this book different
Unlike surface-level AI books, this guide:
* Treats LLMs as systems , not just models
* Explains why design choices exist , not just how
* Covers training, inference, safety, cost, and deployment
* Includes real architecture patterns used in production
* Bridges the gap between research papers and real-world systems
This is the book that explains what happens after the demo works .
If you've ever wondered...How does ChatGPT actually work under the hood?How much does it cost to train or run an LLM?How do companies scale inference to millions of users?How do AI agents' reason and use tools?
This book answers those questions in engineering terms .
Build understanding. Build systems. Build responsibly.
Start building ChatGPT-like systems today.

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