I Have Spent 500+ Hours Programming With AI. This Is what I learned
📚 Main topics
- Learn programming firstAI amplifies what you already know, so programming fundamentals remain essential for getting useful results. 0:30
- Give AI specific instructionsA Google Docs clone experiment showed that supplying technical details, a tech stack, documentation, and design references led to better results than vague or merely general prompts. 1:01
- Break large tasks into smaller onesAI tends to do better with focused tasks, and decomposing a problem is a fundamental engineering skill whether you write the code or delegate it to AI. 5:40
- Use a three-part promptInclude a detailed task, relevant background such as files and documentation, and a clear list of what AI should not change. 7:44
- Give AI project context and toolsProject guidance files can capture the codebase, stack, commands, and workflow; MCP tools can provide documentation and project or browser information. 9:17
- Require verificationHave AI run tests, commands, or the app itself to check its work, and confirm that the verification method is reliable. 11:49
✨ Key takeaways
- Context improves resultsWhen AI has to guess about architecture or styling, it is more likely to produce code that looks plausible but has problems. 4:39
- Preserve human problem-solvingThe speaker is comfortable letting AI type code, but argues that developers still need to think through the solution and correct mistakes. 6:41
- AI amplifies habitsGood engineering habits can make AI more effective, while skipping tests, documentation, and edge cases can amplify existing weaknesses. 12:20
🧠 Lessons learned
- Prompting is part of the workSpending time clarifying the task and its constraints can save time compared with coding a poorly specified solution. 8:45
- Use tools that fit the projectMCPs for documentation, frameworks, or browser tools can help, but the best choices depend on the project and tech stack. 10:17
- Avoid assuming AI will get everything rightEven detailed prompts can leave gaps, so review the output and fix problems rather than relying on it blindly. 5:10
🏁 Conclusion/next steps
- Practice with a real projectTry an AI assistant, apply these techniques, and see which prompting, context, and verification practices improve your workflow. 12:50
Transcript excerpt
0:00 I have spent an unhealthy amount of time programming with AI and I want to share all the tips and tricks I've learned after spending hundreds of hours programming with AI just so you don't have to waste your precious time because I assume you actually have a life unlike me and so you can get the same benefits as I do because a lot of people have been saying they've gotten good results with AI and bad results with AI. And after spending a lot of hours not going outside, not touching grass, I think I finally figured out how to get consistently good results with it. It's definitely better than before, I will [music] say. I'm still learning how this
0:30 tool works, but I at least want to share what I've learned so far with you. So, knowing this, let's start with the first tip. Oh, and this video is sponsored by JetBrains. Tip number one, learn how to program first. Yes, it's an obvious tip, painfully obvious, but I'm going to say it anyway because apparently it needs to be said. If you want AI to be useful at programming, you need to know how to program. This isn't rocket science. This isn't controversial. It's just facts. As of right now, AI is a multiplier. Albeit a small multiplier,
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