Generative AI Prompts
Creating a high-quality signal processing textbook is easy if you use high-quality prompts an leverage the project structure of tools such as ChatGPT and Anthropic Claude. However, creating these high-quality prompts is a non-trivial process and there is a large amount of variation is the quality of the responses generated by different models.
There are about a dozen important techniques that we cover in our Prompt Engineering Course that we will use in the generation of our content. In summary, the more detailed your prompt is and the number of examples of expected output you provide, the higher the probability that even smaller language models will generate suitable output.
Using the COSTAR Framework
If you are not familiar with generating high-quality prompts, you can remember the term "COSTAR" which is an acronym for:
- Context
- Objective
- Style
- Tone
- Audience
- Response
Where these terms mean the following:
- Context - Provide relevant background information and any specific constraints or requirements that will help me understand the task better.
- Objective - Clearly state what you want to achieve with your prompt. Having a well-defined goal helps ensure the response meets your needs.
- Style - Specify any particular writing style or format you'd like the response to follow (e.g., academic, conversational, technical).
- Tone - Indicate the desired emotional tone or attitude (e.g., formal, friendly, authoritative).
- Audience - Identify who the response is intended for, as this helps tailor the content and complexity appropriately.
- Response - Specify the preferred format, length, or structure of the response (e.g., bullet points, paragraphs, specific word count).
https://dmccreary.github.io/prompt-class/glossary/#costar-framework