All right so in today’s video I’ll be talking to a principal researcher at Microsoft

I thought this conversation was quite interesting because we bring such different viewpoints to it. As you know, I always come from the perspective of a power user, a tech enthusiastic consumer that obsesses over finding what’s possible with these tools. She comes from the perspective of a machine learning researcher that works for Microsoft and actually publishes research regularly. As you know, I usually talk about using chat GPT in the web interface and how to find use cases for your everyday life. She’s particularly interested in how to use it for code generation and debugging, so this is a very interesting conversation.

Prompt Engineering Basics

At the core of all interactions with AI models like GPT-3 lies prompt engineering. I like to break prompts down into two parts – instructions and context. Instructions are the tasks you are trying to solve, while context narrows down the possibilities and guides the AI based on the information provided. By defining the context, you unlock the full potential of the AI assistant.

Advanced Techniques: Zero-Shot and Multi-Shot Prompting

Zero-shot and multi-shot prompting are advanced techniques that can enhance the interactions with AI models for tasks like code generation. By providing specific examples and structuring your prompts, you can guide the AI to generate code that fits your needs. Additionally, techniques like Chain of Thought reasoning can help the model understand the logic behind the code snippets it produces.

Custom Instructions and Debugging

Using custom instructions and providing detailed information to the AI assistant can improve the quality of the generated code. Customizing the prompts based on your preferences and providing examples can lead to more accurate results. When it comes to debugging, using follow-up questions and isolating the code can help the model focus on specific issues and provide relevant solutions.

Recommendations for Developers

For developers using AI models for code generation and debugging, it is essential to keep the code compartmentalized and modularized. By isolating functions and asking targeted questions, you can efficiently debug the code and improve the AI’s understanding of the problem. Remember to trust but verify the output and always verify the libraries and functions mentioned in the generated code.

Conclusion

The conversation between the tech enthusiast and the machine learning researcher offers valuable insights into the world of AI-powered code generation. By understanding the basics of prompt engineering, utilizing advanced techniques like multi-shot prompting, and providing custom instructions, developers can enhance their interactions with AI models. Debugging code generated by AI requires a structured approach and targeted questioning to identify and fix errors efficiently.