Summary
In this chapter, we have dived deeper into the concept of prompt design and engineering since it’s the most powerful way to control the output of ChatGPT and OpenAI models. We learned how to leverage different levels of shot learning to make ChatGPT more tailored toward our objectives: if we want the AI response to have a particular style and format, we can provide examples so that it can learn from them, as we saw when analyzing tweet sentiments. We also learned how to write an effective prompt with some nice examples – especially with the Act as... trick – and what to avoid, such as open-ended questions or information overload.
In the next few chapters, we will cover concrete examples of how ChatGPT can boost general users’ daily productivity, with easy prompts and tips you can replicate on your own.
Starting from the next chapter, we will dive deeper into different domains where ChatGPT can boost productivity and have a disruptive impact...
