1 Deploying Large Language Models Reliably in the Real World
This chapter covers
- How large language models (LLMs) work and what they can do.
- How LLMs are reshaping industries.
- Key considerations for reliable and responsible deployment.
- Deploying LLMs responsibly and minimizing hallucinations, bias, inefficiency, and performance bottlenecks.
Flashy AI demos—solving math with voice, critiquing résumés, or planning vacations—grab attention. But most crash when moving from demo to production.
An MIT study reports that 95% of generative AI pilots fail to deliver ROI. Teams hit the same walls: hallucinations, flakey outputs, brittle tools, poor evaluations. AI feels magical in the lab—and unreliable in production.
To stay ahead in the AI revolution, you must master the art and science of reliable LLM implementation. Your journey towards creating more dependable, efficient, and ethical AI solutions starts here. Throughout this book, we’ll build real-world projects—from robust chatbots to capable multi-agent systems—grounded in practical engineering techniques you can apply immediately. Whether you're a software engineer, data scientist, or ML practitioner, you’ll gain techniques to improve AI reliability and performance.