Summary
In this chapter, we first talked about the problem of hallucinations and automatic fact-checking, and how to make LLMs more reliable. We implemented a few simple approaches that help to make LLM outputs more accurate. We then looked at and implemented prompting strategies to break down and summarize documents. This can be immensely helpful for digesting large research articles or analyses. Once we get into making a lot of chained calls to LLMs, this can mean we incur a lot of costs. Therefore, I dedicated a subsection to token usage.
The OpenAI API implements functions, which we can use, among other things, for information extraction in documents. We’ve implemented a remarkably simple version of a CV parser as an example of this functionality that indicates how this could be applied. Tools and function calling are not unique to OpenAI, however. The evolution of instruction tuning, function calling, and tool usage enables models to move beyond freeform text generation...
