LLM Adoption in the Enterprise

Chapter 4. The AI Team’s Toolkit

So far, we’ve focused on the soft skills your adoption process will require, such as intuition, observation, and effective cross-functional communication. But your AI adoption cannot be successful without hard skills as well. Let’s say you’ve assembled the kind of cross-functional team described in the previous chapter that’s needed to get your product off the ground. Each role has its own unique set of skills. When we focus on AI teams, especially AI engineers, we’re considering a wide range of interests, talents, and tools. In this chapter, I want to explore the key components of this diverse skill set.

In addition to keeping up with the latest developments in language modeling, AI engineers are often very good prompters, thanks to their hands-on experience getting LLMs to do their bidding. Because effective prompting is so important to getting the results you want, avoiding hallucinations, and even saving money, I will look at it from a slightly different perspective in this chapter: prompting as programming in natural language. Then, we’ll move on to the most valuable resource of our time—data—and best practices for AI teams to take care of their organization’s data assets. We’ll see why well-governed and curated data is a necessity for the vast majority of generative AI applications in the enterprise. Finally, I’ll talk about how the composability principle of modern-day natural language processing (NLP) designs allows AI engineers to create ...