This chapter is from the book
This chapter is from the book
This chapter is from the book
Summary
Prompt engineering—the process of designing and optimizing prompts to improve the performance of language models—can be fun, iterative, and sometimes tricky. We saw many tips and tricks for how to get started, such as understanding alignment, just asking, few-shot learning, output structuring, prompting personas, and working with prompts across models. We also built our own chatbot using ChatGPT’s prompt interface, which was able to tie into the API we built in the last chapter.
There is a strong correlation between proficient prompt engineering and effective writing. A well-crafted prompt provides the model with clear instructions, resulting in an output that closely aligns with the desired response. When a human can comprehend and create the expected output from a given prompt, that outcome is indicative of a well-structured and useful prompt for the LLM. However, if a prompt allows for multiple responses or is in general vague, then it is likely too ambiguous for an LLM. This parallel between prompt engineering and writing highlights that the art of writing effective prompts is more like crafting data annotation guidelines or engaging in skillful writing than it is similar to traditional engineering practices.
Prompt engineering is an important process for improving the performance of language models. By designing and optimizing prompts, you can ensure that your language models will better understand and respond to user inputs. In Chapter 5, we will revisit prompt engineering with some more advanced topics like LLM output validation, chain-of-thought prompting to force an LLM to think aloud, and chaining multiple prompts together into larger workflows.