AI Engineering

Chapter 1. Introduction to Building AI Applications with Foundation Models

If I could use only one word to describe AI post-2020, it’d be scale. The AI models behind applications like ChatGPT, Google’s Gemini, and Midjourney are at such a scale that they’re consuming a nontrivial portion of the world’s electricity, and we’re at risk of running out of publicly available internet data to train them.

The scaling up of AI models has two major consequences. First, AI models are becoming more powerful and capable of more tasks, enabling more applications. More people and teams leverage AI to increase productivity, create economic value, and improve quality of life.

Second, training large language models (LLMs) requires data, compute resources, and specialized talent that only a few organizations can afford. This has led to the emergence of model as a service: models developed by these few organizations are made available for others to use as a service. Anyone who wishes to leverage AI to build applications can now use these models to do so without having to invest up front in building a model.

In short, the demand for AI applications has increased while the barrier to entry for building AI applications has decreased. This has turned AI engineering—the process of building applications on top of readily available models—into one of the fastest-growing engineering disciplines.

Building applications on top of machine learning (ML) models isn’t new. Long before LLMs became prominent, AI ...