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8: I’ve Been Using AI to Code for a Year. Here’s What I Learned
Wolf has a lot to say about AI and coding!
If you’re expecting AI to write entire programs for you while you sit back and watch, we're going to disappoint you right up front—that’s not what these tools do well, and chasing that fantasy will waste your time. But what if we told you there’s a tool that could help you interpret cryptic error messages, navigate tricky syntax in unfamiliar languages, write the documentation you always skip, and spot those needle-in-the-haystack bugs that eat hours of your day—but only if you understand what you’re asking it to do? Today we’re diving into the reality of AI tools for programmers: not the magic bullet some promise, but the practical truth about what actually works and how to avoid the pitfalls that can corrupt your work.
Wolf has been using AI tools in his daily programming work, and he wants to share what he's learned—both the genuine benefits that have made him faster and more effective, and the critical mistakes that can trip you up if you’re not careful. We’ll explore why AI isn’t magic, why being a domain expert matters more than ever, and how these tools are more like really sophisticated auto-complete than the thinking machines some claim them to be. Whether you’re skeptical, curious, or already experimenting, you’ll walk away with concrete strategies for making AI work for you instead of against you.
Take-aways from the episode:
- You must be a domain expert. You must understand the problem, and you must have the knowledge and skill to evaluate the result. This might be the most important thing I’ve said in this whole episode.
- You must read and understand everything the AI gives you. This is fundamental. It’s part of your job. It always has been.
- Working with AI is an iterative collaboration, not just pushing a button on the magic “answer machine”. You will be doing a lot of work if the problem is hard. And it’s the same work you’ve been doing, maybe all your life: explaining to the computer exactly what you want.
- You must carefully, fully, and deeply explain to the AI what you want (Prompt Engineering), and even then, you (plural) will iterate over and over and over again before reaching a satisfying answer.
- Current AIs don’t think, they produce the most popular results they can that satisfy your prompt. They don’t give you the best answer. They give you the most popular answer.
- AI is great at analyzing and helping you to improve your existing code. It’s less great when you start with nothing.
Hosts:
Jim McQuillan can be reached at [email protected]
Wolf can be reached at [email protected]
Follow us on Mastodon: @[email protected]
If you have feedback for us, please send it to [email protected]
Checkout our webpage at http://RuntimeArguments.fm
Theme music:
Dawn by nuer self, from the album Digital Sky
View all episodes
By Jim McQuillan & WolfWolf has a lot to say about AI and coding!
If you’re expecting AI to write entire programs for you while you sit back and watch, we're going to disappoint you right up front—that’s not what these tools do well, and chasing that fantasy will waste your time. But what if we told you there’s a tool that could help you interpret cryptic error messages, navigate tricky syntax in unfamiliar languages, write the documentation you always skip, and spot those needle-in-the-haystack bugs that eat hours of your day—but only if you understand what you’re asking it to do? Today we’re diving into the reality of AI tools for programmers: not the magic bullet some promise, but the practical truth about what actually works and how to avoid the pitfalls that can corrupt your work.
Wolf has been using AI tools in his daily programming work, and he wants to share what he's learned—both the genuine benefits that have made him faster and more effective, and the critical mistakes that can trip you up if you’re not careful. We’ll explore why AI isn’t magic, why being a domain expert matters more than ever, and how these tools are more like really sophisticated auto-complete than the thinking machines some claim them to be. Whether you’re skeptical, curious, or already experimenting, you’ll walk away with concrete strategies for making AI work for you instead of against you.
Take-aways from the episode:
- You must be a domain expert. You must understand the problem, and you must have the knowledge and skill to evaluate the result. This might be the most important thing I’ve said in this whole episode.
- You must read and understand everything the AI gives you. This is fundamental. It’s part of your job. It always has been.
- Working with AI is an iterative collaboration, not just pushing a button on the magic “answer machine”. You will be doing a lot of work if the problem is hard. And it’s the same work you’ve been doing, maybe all your life: explaining to the computer exactly what you want.
- You must carefully, fully, and deeply explain to the AI what you want (Prompt Engineering), and even then, you (plural) will iterate over and over and over again before reaching a satisfying answer.
- Current AIs don’t think, they produce the most popular results they can that satisfy your prompt. They don’t give you the best answer. They give you the most popular answer.
- AI is great at analyzing and helping you to improve your existing code. It’s less great when you start with nothing.
Hosts:
Jim McQuillan can be reached at [email protected]
Wolf can be reached at [email protected]
Follow us on Mastodon: @[email protected]
If you have feedback for us, please send it to [email protected]
Checkout our webpage at http://RuntimeArguments.fm
Theme music:
Dawn by nuer self, from the album Digital Sky