LLM Engineering - Production Systems for the Agentic Era: Fine-Tuning, RAG, Agents, and LLMOps

Instead of focusing on isolated techniques, this book gives you a complete systems-level approach-combining fine-tuning, retrieval, agents, and LLMOps into one cohesive engineering discipline.

• Design LLM systems that work reliably beyond the "happy path"
• Choose between prompting, RAG, and fine-tuning with clear decision frameworks
• Build scalable inference pipelines with predictable latency and cost
• Fine-tune models efficiently using LoRA, QLoRA, and modern alignment methods
• Implement retrieval systems that actually improve accuracy (not just complexity)
• Architect agent systems that can plan, act, and recover from failure
• Evaluate LLM outputs with rigorous, production-ready metrics
• Optimize performance across cost, latency, and quality trade-offs
• Deploy, monitor, and maintain LLM systems using real LLMOps practices

• A model routing system that dynamically selects the cheapest model for each task
• A complete fine-tuning pipeline with evaluation and deployment
• A production-grade RAG system with hybrid retrieval and re-ranking
• A multi-agent system with tool use and orchestration
• A full LLM production stack deployed with observability, guardrails, and CI/CD

This book is for software engineers, ML engineers, AI practitioners, and technical leaders who are moving from experimentation to real-world deployment.

If you already understand the basics of LLMs but struggle with reliability, scaling, or system design-this is the missing piece.

What sets this book apart:

• It focuses on production systems, not toy examples
• It provides end-to-end architecture, not isolated techniques
• It emphasizes engineering trade-offs and decision-making, not hype

By the time you finish the book, you would have learned how LLMs work- how to ship them, scale them, and trust them in production.