chapter one
1 AI Reliability: Building LLMs for the Real World
This chapter covers
- Defining reliability for production AI systems and why it matters now
- Navigating the new landscape: reasoning models, coding agents, and autonomous systems
- Diagnosing hallucinations: why LLMs fabricate information and how to detect it
- Applying a three-layer reliability framework across outputs, agents, and operations
- Building your reliability toolbox
We are living through one of the most significant capability jumps in the history of artificial intelligence.
Just a few years ago, the best AI models could write decent essays and answer questions. Today, they can reason through PhD-level mathematics, write production-quality code, browse the web autonomously, and coordinate complex multi-step tasks across dozens of tools. Modern LLMs (large language models like OpenAI’s GPT, Anthropic’s Claude, and Google’s Gemini) don’t just generate text. They reason through multi-step problems, plan sequences of actions, use external tools, and take real-world actions.