Prompt Engineering pertains to the process of making instructions to produce the best output from a generative AI model.

Techniques for Prompting

These techniques exist because not all prompts lead to the same accuracy.
The best way to use LLMs is not to craft perfect prompts, but to use LLMs interactively, allowing it to modify its output.
The trick is to give the system context and constraints. This introduces specificity in the response.
- Give the system a role. Tell the system “who” it is.
- Add constraints to writing style such as by having it phrase it in a specific way or avoid repeating itself.
- Provide data as additional context. This can be used for summarization tasks as well.
Think about prompting as programming in English. Give the AI instructions.
- Use CoT Prompting where the AI is given an example of how it is to reason before making the request.
Some fun things to ask the AI:
- To make any assumptions it needs.
- To remove practical constraints.
- To provide sources for responses.
- To state how to do something step-by-step.
- Tell a developer how to use its generated code.
- To write a draft or provide an example.
Prompt Engineering appears to be a product of scale.

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