Ask HN: May be a basic question, but how can I use AI well?
8 comments
It's important to recognize that there are no optimal "right" answers. If someone is telling you there is a best way of doing this, always treat their advice with suspicion. (Yes, this includes what I'm going to write below.) The reason is that because AI advances so quickly, there isn't enough time for the industry to stabilize on best practices and spread them widely before the underlying system changes and it's no longer applicable.
Because of this, I find it very important that you build your own understanding of LLMs, and create your own best practices from these first principles. Then, when you read about the next best thing, you can decide if it makes sense or not. Is it hype? Is it real? Does it logically align with what I understand about LLMs?
That being said, here's two things I find universally applicable:
1. You have to get very good at asking questions. Not only that, you also have to ask questions about the questions you should be asking. You also have to ask it to ask YOU questions.
2. Spec driven development is, in my opinion, a good place to start. Writing down what you're going to do and how you're going to do it has always been a good practice in any industry.
Because of this, I find it very important that you build your own understanding of LLMs, and create your own best practices from these first principles. Then, when you read about the next best thing, you can decide if it makes sense or not. Is it hype? Is it real? Does it logically align with what I understand about LLMs?
That being said, here's two things I find universally applicable:
1. You have to get very good at asking questions. Not only that, you also have to ask questions about the questions you should be asking. You also have to ask it to ask YOU questions.
2. Spec driven development is, in my opinion, a good place to start. Writing down what you're going to do and how you're going to do it has always been a good practice in any industry.
Spend time thinking through your daily work routine and create a list of toil items. Try to describe the toil and steps you usually take when tackling it and write them out in a markdown file. Tell your model of choice where to find the file and start engaging in a two way conversation with it.
Go from there.
Go from there.
thanks!
So I am taking a different approach than instead of just focusing on software. I have Gemini app, Claude app, Grok app and then chatgpt still using web interface. And periodically I ask them questions about what I can do to improve the wellbeing of humans. I think trying to use them for work is okay, but it's not the best way to use these tools, we can use them to broaden our thinking and find challenges we never considered before.
My approach is to try delegating as many daily tasks to AI as possible. For example, I've now completely replaced my read-it-later tool with my agent. I just send him links, and he periodically summarizes the content I've sent him.
Because of the nature of this work, it is very labor intensive. There are too many factory trips, it is physically exhausting, and the pay is not great relative to the amount of effort involved. That is why I want to become more of a product oriented programmer. To move in that direction, I have been trying to use AI as actively as possible. In my current job, though, that is difficult because factory security is usually closed off and AI tools cannot really be connected there.
I have been reading about prompt engineering, harness style documentation, and similar topics. I also use OpenClaw, and honestly I feel like I am already trying almost everything I can get my hands on. I connect it with Obsidian and write down the knowledge I think my agents need while I work.
Still, the idea of ten times productivity feels somewhat exaggerated to me. I want to know how people actually get better at using AI, and how they learn the underlying methods. There is so much hype around AI that it is hard to tell what is real, and learning this stuff has been more difficult than I expected.
How should I study this properly?
Right now I especially want to learn how to manage multi agent systems. Every AI only multi agent framework I have built or tried so far has failed. At first I tried to control it in a TDD like way, but people who have used TDD seriously probably know what I mean when I say that tests can become too locally focused. Sometimes the architecture starts to fall apart, and then the agents keep fixing only those small areas over and over while the token cost keeps rising.
At the same time, in Korea there is a huge amount of talk that if you are not using AI, you will fall behind. Because of that, I have been trying hard to learn it so I do not get left behind. And to be honest, programming has become much more enjoyable for me since I started using AI.
One reason is that programming feels deeply tied to English ways of thinking, and that has always felt awkward with Korean. Even the act of writing code used to feel mentally heavy for me. But with AI, I can think through things in Korean and still code effectively.
You know that huge fatigue you feel when you first start writing code from scratch?
When I write a single interface, I immediately start seeing the number of implementations. When I see the implementations, I start seeing lifecycle conflicts. When I see lifecycle issues, I start thinking about ownership and disposal. When I think about ownership, I start thinking about pooling possibilities and reset contracts. When I think about DI, I start seeing the composition root and the test seams. When I think about the contract itself, I start worrying about future extension costs.
All of that used to make coding feel painfully heavy for me.
But AI just writes a draft without getting stuck in all of that, and I genuinely enjoy taking that draft and reshaping it around my own thinking.
I want to get much better at using AI this way.
What are the best ways to improve at it, and how do you keep up with useful trends without getting buried in the hype?