chapter one
1 AI reliability: Building LLMs for the real world
This chapter covers
- Defining reliability for production AI systems and seeing 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 multistep tasks across dozens of tools. Modern large language models (LLMs) such as OpenAI’s GPT, Anthropic’s Claude, and Google’s Gemini don’t only generate text. They also reason through multistep problems, plan sequences of actions, use external tools, and take real-world actions.