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"Stack Overflow on steroids." That’s the best way to describe it. But is AI the ultimate productivity hack, or just a faster way to create complex code? From parsing tricky regex in seconds to figuring out how much context to feed the bot, integrating these tools is a skill we are all learning in real-time.
I sat down (virtually) to discuss the art of prompt engineering and the nuances of relying on generated solutions. We dive into why using AI effectively isn't just about getting answers, it's about knowing how to ask the right questions to build better, sustainable software.
*Note: This interview with Dan was conducted by Claude, an AI assistant. Yes, the irony of using AI to discuss using AI is not lost on us.*
Let's start with the basics: How would you describe AI as a development tool in one sentence?
"Stack Overflow on steroids" - honestly, that's what it is for me. It's like having that incredibly knowledgeable senior developer available 24/7, but you need to know how to talk to them.
So where does AI for developers shine day-to-day?
For quick, isolated problems, it's incredible. Need to parse a tricky regex? Convert between date formats? Understand an error message? Just ask. No context needed, instant answer. These small, specific queries are where AI shines brightest with minimal effort.
What are the downsides of using AI for coding?
The big challenge is context, especially for larger projects. That's where things get interesting - and harder.
Tell me about context. How did you initially approach giving context for bigger features?
My initial approach was exhausting. I'd dump everything into the prompt: "Here's my entire file structure, here's the relevant code, here's what I'm trying to do..." I'd spend 10 minutes crafting a prompt, constantly worried I'd forget some crucial detail. It worked, but it was draining.
So what changed? What's a better approach to prompt engineering?
A fellow developer showed me a completely different approach: let the AI build its own context through questions. Instead of front-loading everything, you start with something simple like "I need to add authentication to my Express app," and the AI asks what it needs to know - what auth strategy, existing middleware, whatever. The conversation naturally builds the needed context. You only provide what's actually relevant.
That's a smart approach. Has that solved the context problem for you?
Honestly? I'm still figuring it out. The truth is, I still struggle with prompt engineering. For larger features or refactors, I haven't found the perfect workflow. Sometimes the "let AI ask questions" approach works great. Other times, I need to provide upfront context. It really depends on the complexity and how well I can articulate what I need.
If prompt engineering is hard, why keep using AI?
Because it's not about getting complete solutions - it's about changing how I problem-solve. It's like having a knowledgeable pair programmer who's always available. The skill isn't in knowing everything anymore; it's in knowing what questions to ask and when to ask them.
What are you seeing around you? How are other developers using AI tools?
It's wild, honestly. I've seen colleagues build entire projects from scratch in just days, even hours. I've also seen someone migrate a whole Angular project to a newer technology without writing a single line of code themselves. It sounds amazing, right?
But here's the catch - if you don't review what AI generates carefully, you can accumulate massive technical debt. The code works, sure, but it might not follow your team's patterns, might have hidden performance issues, or make architectural decisions that don't fit your specific needs. AI can move fast, but someone still needs to be the critical reviewer.
Final question - What's next for AI in development?
I'm still learning, we all are. The tools are evolving, and so is my approach to using them. But one thing's clear: understanding how to interact with AI is becoming as important as understanding the code itself. It's a skill we're all developing in real-time.
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