Handling Revision Prompts - Iterative Code Generation with LLMs
Offered By: Jeff Heaton via YouTube
Course Description
Overview
Explore the process of conversational code generation using Large Language Models (LLMs) in this 10-minute video tutorial. Learn how to refine and iterate on code prompts, interactively request changes to generated code, and consolidate conversational history into a single prompt for maintainable code generation in complex projects. Dive into practical examples, such as building a PyTorch neural network to approximate the XOR function, and discover how to guide LLMs with follow-up prompts to fine-tune models for better results. Gain insights into 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.
Syllabus
Handling Revision Prompts (2.2)
Taught by
Jeff Heaton
Related Courses
Neural Networks for Machine LearningUniversity of Toronto via Coursera Good Brain, Bad Brain: Basics
University of Birmingham via FutureLearn Statistical Learning with R
Stanford University via edX Machine Learning 1—Supervised Learning
Brown University via Udacity Fundamentals of Neuroscience, Part 2: Neurons and Networks
Harvard University via edX