Prompt Engineering for Code Generation

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AI Nuggets

Based on the transcript provided, here is the extracted information in an easy-to-follow outline form:

Code Generation Use Case for Prompt Engineering

Task Definition

• Generate Python code that takes two integers and a string representing an operation, then returns the result of that operation.

Evaluation Metric

• Use test cases with predefined inputs and outputs to evaluate the quality of the generated code.

Test Cases Example

• Function called calculate takes in numbers (e.g., 5 and 3) and an operation symbol (e.g., +), and the expected output is the result of the operation (e.g., 8).

Prompt Creation

• System message for the model: Define a Python code generation engine that responds to prompts with code descriptions or half-finished code.
• Prompt to generate code: Begin with a partial function and use HTML-style tags to indicate the structure of the desired response.

Experimentation Process

• Set up for calling the CHP API with the system message and prompt.
• Use GPT-4 for code generation.
• Simplify the process without setting parameters at the beginning or having a prompt engineering experiment pipeline.

Code Generation and Execution

• Generate the Python code and print it with markdown.
• Extract the Python code from the markdown using a simple regex extraction.
• Execute the generated Python code using Python’s exec method.
  • Caution: Executing untrusted code should be done in an isolated or sandbox environment for safety.

Testing the Generated Code

• Access the calculate function in the Jupyter notebook environment.
• Run unit tests to check if the function passes the predefined test cases.

Iteration and Evolution

• Add functionality by adding tests, prompting the model to generate the function, and testing against the new test cases.
• Incorporate model-generated refactors and additions, ensuring they pass tests before making a push.

Recommended Papers

• ”AI-Driven Development” (published earlier in the year).
• ”Alpha Codium” (popular paper on AI-driven development pipelines).

Next Steps

• The next video will demonstrate understanding research papers using prompt engineering.

URLs and CLI Commands

• The video URL for additional information: http://youtube.com/watch?v=n-dWSfvFQtk (No additional information extracted as the URL is not accessible for browsing).

Tips

• When executing untrusted code, always do so in an isolated or sandbox environment to prevent potential issues.
• Iteratively add functionality and tests to ensure the generated code meets the required standards before deployment.

(Note: The actual CLI commands, specific regex patterns, and URLs for the CHP API or other resources were not provided in the transcript, hence they are not included in this outline.)