Handling Revision Prompts (2.2)

Опубликовано: 17 Март 2025
на канале: Jeff Heaton

671

In this video, we explore the process of conversational code generation using LLMs, focusing on refining and iterating on code prompts. We start by examining how to interactively request changes to generated code, ensuring the LLM produces more accurate and tailored outputs. This module demonstrates the benefits of consolidating your conversational history into a single prompt, which can help maintainable code generation in complex projects.

We'll dive into practical examples, such as building a PyTorch neural network to approximate the XOR function, using the LLM to make adjustments and improvements to the code based on feedback. By guiding the LLM with follow-up prompts, we fine-tune the model to deliver better results. This video also highlights best practices for formatting and structuring prompts, ensuring your final code meets industry standards, like PEP-8, and includes well-structured comments and sorted imports.

Code for This Video:
https://github.com/jeffheaton/app_gen...

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