Large Language Models for Developers

Large language models are transforming software development-but building a useful LLM application requires much more than writing a clever prompt.
Large Language Models for Developers is a practical, engineering-focused guide to understanding how modern language models work and using them to build reliable AI applications. From transformers and embeddings to retrieval-augmented generation (RAG), AI agents, fine-tuning, evaluation, security, and production deployment, this book connects LLM fundamentals with real application engineering.
You will begin by understanding the technologies behind modern LLMs, including tokenization, embeddings, transformer architecture, self-attention, training, inference, context windows, and text generation. From there, you will move into the techniques developers need to build complete LLM-powered systems.
Inside, you will learn how to:
• Understand transformers, attention, tokens, embeddings, and vector representations
• Work with LLM APIs and build reusable model interfaces
• Design prompts for reliable and structured application behavior
• Build semantic search with embeddings and vector databases
• Engineer retrieval-augmented generation (RAG) pipelines
• Ground AI responses in external knowledge and retrieved evidence
• Connect LLMs to APIs, databases, services, and software tools
• Build AI agents with tools, state, memory, planning, and controlled autonomy
• Fine-tune and adapt language models with supervised fine-tuning and LoRA
• Evaluate accuracy, relevance, groundedness, retrieval quality, and agent behavior
• Protect LLM applications against prompt injection, unsafe outputs, and excessive permissions
• Design authentication, authorization, privacy, and security controls
• Monitor application quality, latency, reliability, and cost
• Test, deploy, scale, and maintain production LLM applications
Rather than presenting prompting, RAG, agents, and fine-tuning as isolated techniques, this book shows how to choose between them-and how to combine them when building real systems.
Practical Python examples, architectural patterns, evaluation methods, and production-oriented guidance demonstrate how the pieces fit together. The focus is not simply on making an LLM generate impressive responses, but on engineering applications that are grounded, testable, observable, secure, maintainable, and reliable.
Whether you are a software developer, Python programmer, AI engineer, machine learning practitioner, or technical professional entering generative AI development, Large Language Models for Developers provides a structured path from LLM fundamentals to production application engineering.
Go beyond prompting. Learn how to engineer complete LLM-powered applications.