Prompt Engineering

Prompt Engineering requires an understanding of how AI models process information. Developers must know the capabilities and limitations of the AI tools they are using to craft effective prompts. This understanding ensures that the AI is leveraged within its optimal operational parameters, avoiding misinterpretations or incorrect outputs.

Best Practices

  1. Clarity and Precision
  2. Context matters
  3. Iterative Refinement
  4. Feedback Integration

Examples for various use cases

reference

Example for coding

  1. Introduction: Set up the context for which you’re chatting in. It helps to give the AI an imaginary ‘role’ to think of themselves in. e.g. “Act as a software engineer. You’re an expert in Python and …”
  2. Task: e.g. “I want you to develop software to manage my record collection.”
  3. Contextual Information: e.g. “I want it to be a web based application written in Python.”
  4. Instructions: e.g. “I want you to generate the code to write the program.”
  5. Closing: e.g. “I want to host it as an AWS Lambda function.”
Act as a software engineer. 
You're an expert in Python and AWS technologies. 
I want you to develop software to manage my record collection. 
Make it a web based application written in Python. 
Generate the code for the program, and I want to host it as a Lambda function.