The AI Engineer's Blueprint: From First Principles to Production Systems — Building Real-World AI with LLMs, RAG, AI Agents, and Evals

Stop building AI demos. Start shipping AI systems. Now fully revised and expanded.

The Second Edition of The AI Engineer's Blueprint doesn't just teach you production AI—you build one, chapter by chapter. A companion code repository turns the book's concepts into a working, deployed, evaluated AI assistant that grows with every part: from a first API call, to grounded retrieval, to a tool-using agent, to a guarded, observable, cost-controlled production service.

New in the Second Edition:

Companion GitHub repository — Build a complete capstone project throughout the book that you can showcase in interviews and your portfolio.
Reasoning models & extended thinking — Learn when advanced reasoning models help, what they cost, and how to route requests effectively.
Context engineering — Master the art of managing context, not just writing better prompts.
Fine-tuning vs. RAG vs. prompting — Understand the trade-offs and choose the right approach for real-world problems.
Multimodal & voice AI — Build applications that work with images, documents, and speech.
Cost engineering — Optimize AI systems with prompt caching, semantic caching, model routing, and AI gateways.
Complete interview guide — Practice with topic-wise question banks, AI system design interviews, and a portfolio playbook.

Also included, fully updated:

Machine learning, deep learning, and transformer fundamentals explained for software engineers
Large Language Models (LLMs), embeddings, vector databases, and Retrieval-Augmented Generation (RAG)
AI agents, tool calling, memory, and the Model Context Protocol (MCP)
Evaluation, guardrails, grounding, AI safety, and prompt injection defense
Deploying AI systems with observability, reliability, scalability, and major cloud AI platforms

Who it's for:

Software Engineers
AI Engineers (Aspiring and Experienced)
Data Scientists
Machine Learning Engineers
DevOps and Platform Engineers
Engineering Students and Professionals transitioning into AI

No PhD required—just programming experience and a desire to build real AI systems.

If you build one serious AI system in your career, this is the book—and the accompanying codebase—that will prepare you to build it with confidence.