Working with LLMs
Overview
In this part, we look into working with large language models by providing them inputs — prompts — that guide in creation of outputs that are more in line with our expectations.
The chapters in this part are as follows.
- Prompting Basics & Taxonomy introduces prompting and showcases different types of prompts, discussing how the prompt influences the outputs of large language models.
- Clear Instructions and Context discusses the need to provide clear instructions to large language models, including the use of contextualization and persona.
- In-context Learning introduces the concept of in-context learning, and discusses zero-shot, one-shot, and few-shot prompting.
- Reference Text and Retrieval-Augmented Generation outlines how the outputs of large language models can be constrained to given input text.
- Reasoning Through Problems provides an example of chain-of-thought prompting, a technique where large language models are provided some information of how to determine the answer.
- Prompt Chaining discusses the use of multiple prompts in a sequence, where the output of one prompt is used as the input to the next prompt.
- Limitations of Prompting discusses the fundamental limitations of prompting, and what prompting cannot solve.
- Summary summarizes the key takeaways from this part.