Part 3 Advanced topics

This part of the book will help you write applications with language models that are more effective and cost-efficient.

Chapter 8 broadens our scope from OpenAI’s language models to other providers. Before applying language models to large data sets, it is crucial to compare models offered by different providers. That way, you get the optimal tradeoff between cost and quality for your specific scenario. The chapter discusses some of the most popular providers, their models, and the libraries they offer.

Chapter 9 demonstrates techniques for cost optimization in an example scenario. It covers topics such as prompt engineering, the optimal tuning of model configuration parameters, and fine-tuning, a process by which a language model is specialized for one specific task. As shown in this chapter, using those methods can yield significant improvements in terms of quality and cost.

Chapter 10 discusses two software frameworks that have become very popular for developing complex applications on top of language models: LangChain and LlamaIndex. Both can be useful for data analysis. In particular, the chapter shows how to use those frameworks to build agents, an approach that enables language models to solve complex data analysis tasks using a variety of computational tools.