Approach: AI Assisted Development

Building a product or proof of concept with AI as your coding partner can dramatically accelerate development — but only if you approach it with the right habits. Here's how to get the most out of AI-assisted development without painting yourself into a corner.

Start with Clear Problem Framing, Not Code
Before you open a chat window and ask for code, spend time articulating the problem you're solving and who you're solving it for. AI is remarkably good at generating solutions, but it can't validate whether you're solving the right problem. Write a short brief — even a few sentences — covering the user need, the core workflow, and what "done" looks like. Feed that context to the AI and let it help you pressure-test assumptions before a single line of code is written.

Use AI for Divergent Thinking First
One of AI's underused strengths is rapid ideation. Before committing to an architecture, ask it to propose three or four fundamentally different approaches to your problem. You might ask for a comparison of a server-rendered app versus a single-page app versus a CLI tool for the same use case. This is where AI shines — it can sketch out trade-offs in minutes that might take you hours to research. Evaluate the options with your own judgment, then converge on an approach.

Be Specific With Context and Constraints
Vague prompts produce generic code. The more you tell the AI about your stack, your constraints, your target users, and your existing codebase, the more useful its output becomes. Instead of "build me a login page," try "generate a login page using React and Tailwind that supports email/password and Google OAuth, stores sessions in HTTP-only cookies, and follows our existing component patterns." Attach relevant files or describe them. Context is the single biggest lever you have over output quality.
Treat AI Output as a Draft, Not a Deliverable
AI-generated code is a starting point. It will often look correct, run on the first try, and still contain subtle issues — hardcoded values, missing edge cases, insecure defaults, or patterns that won't scale. Read every line. Understand what it does and why. Refactor what doesn't fit your standards. The goal is to accelerate your workflow, not to outsource your judgment.
Build Incrementally and Validate Often
Resist the temptation to generate an entire application in one shot. Instead, work in small, testable increments: get a data model right, then the API layer, then the UI. Run the code after each step. When something breaks, share the error with the AI along with the relevant context and iterate. This tight feedback loop produces far better results than a single massive prompt and prevents compounding errors that become difficult to untangle.
Keep Architecture Decisions in Your Hands
AI can suggest architectures, but it doesn't understand the full picture of your team's skills, your deployment environment, your compliance requirements, or your long-term roadmap. Use AI to inform these decisions, but own them yourself. Document your architectural choices and the reasoning behind them — this becomes especially important when the POC evolves into a real product.
Use AI to Write Tests and Documentation Too
Don't just use AI for production code. Ask it to generate unit tests, integration tests, and documentation alongside the features it builds. This catches bugs early, forces clearer thinking about expected behavior, and leaves you with a much stronger foundation if the proof of concept gets the green light to continue.
Know When to Stop Prompting and Start Coding!
Sometimes the fastest path forward is to write the code yourself. If you've spent several rounds going back and forth trying to get the AI to produce exactly what you want, it may be quicker to take what you have, open your editor, and finish it manually. AI is a tool, not a replacement for craft. The best results come from developers who fluidly move between generating, reviewing, editing, and writing on their own.

AI-assisted development is a genuine force multiplier for building products and proofs of concept. The teams getting the best results aren't the ones who prompt the hardest — they're the ones who think clearly about what they're building, stay hands-on with the output, and use AI to remove friction rather than replace understanding.