5.0 Pitfalls & Best Practices When Using LLMs

Large Language Models (LLMs) have quickly moved from research labs into real-world products— powering chatbots, copilots, content creation tools, and enterprise workflows. Their ability to understand and generate natural language at scale is astonishing.

But here’s the uncomfortable truth: LLMs are not risk-free. Behind the impressive fluency lie challenges that can derail projects if left unaddressed. From hidden biases to spiraling compute costs, from latency bottlenecks to governance blind spots— deploying LLMs in practice demands more than technical wiring.

In Chapter 5 of the book, I unpack the hidden pitfalls that organizations often underestimate, and outline proven best practices to navigate them. Here’s a glimpse.

What You’ll Discover in This Chapter

1. Bias and Ethical Considerations

LLMs inherit patterns from the vast data they’re trained on—which means they can reproduce bias, stereotypes, or culturally inappropriate content. In sensitive domains like hiring, education, or healthcare, the stakes are high. This section explores concrete strategies for recognizing and reducing these risks, making fairness a design principle rather than an afterthought.

2. Compute Resources and Cost Management

Training and serving large models is expensive. Even inference costs can pile up fast in production. This part breaks down practical approaches to right-sizing your model, optimizing infrastructure, and keeping deployments sustainable—both financially and environmentally.

3. Real-Time Latency Challenges

Users expect instant answers. But LLMs can be slow, and delays erode trust. You’ll learn architectural tricks and algorithmic techniques to reduce latency while preserving response quality, making your system feel responsive at scale.

Why This Matters

Deploying LLMs responsibly means thinking beyond raw accuracy scores. Success comes from balancing fairness, cost, responsiveness, and trust. Choosing a model is only the starting point—operational excellence and ethical rigor are what make your AI dependable.

This chapter gives you the practical playbook for avoiding the most common mistakes and building AI systems that last.