chapter one
1 AI Reliability: Building LLMs for the Real World
This chapter covers
- What makes an AI system reliable - and why it matters for production deployments
- The explosion of AI capabilities: from reasoning models to autonomous agents
- Understanding hallucinations: why LLMs fabricate information and how to detect it
- A practical framework for thinking about AI reliability across outputs, agents, and operations
- The reliability toolbox: techniques you'll master throughout this book
We are living through the most dramatic capability jump 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 like GPT, Claude, and Gemini don't just generate text, they think, plan, and act.
Yet an MIT study reports that 95% of generative AI pilots fail to deliver ROI. Teams hit the same walls: hallucinations, flaky outputs, brittle tools, poor evaluations. AI feels magical in the lab and unreliable in production [11].