The Art of AI-Assisted Development: A Practical Guide to What Works (and What Does Not)
Artificial Intelligence has revolutionized software development, but like any powerful tool, it requires skill and understanding to use effectively. After working with AI coding assistants extensively, I’ve learned that success isn’t about generating more code faster—it’s about using AI strategically while maintaining human oversight and judgment.
Let me share what I’ve discovered about optimal AI usage across the two most critical phases of development: planning and implementation.
Planning: The Foundation of Success
The Tempting Pitfalls
We’ve all been tempted by the promise: feed AI a few sentences and watch it produce a comprehensive Product Requirements Document. It seems magical—until reality hits.
The truth is, asking AI to generate an elaborate plan from minimal context is a recipe for disaster. The AI doesn’t have a crystal ball; it will fill knowledge gaps with educated guesses and hallucinations. Consider this: no successful project in history was built by giving a client’s two-paragraph explanation to a team and expecting perfection at delivery. You wouldn’t buy a house by texting an agent two paragraphs and your bank details—so why would software development be any different?
Even when AI produces something that looks impressive, the danger isn’t over. Accepting these generated plans without verification creates another problem: you’ve essentially recreated the Waterfall methodology that the industry moved away from decades ago. Remember why Waterfall failed? It assumed we could plan everything perfectly upfront without iterative feedback. An AI-generated PRD followed by AI-generated tasks is just Waterfall with extra steps.
The Path That Actually Works
Effective planning with AI requires humans to lead and direct. Start by writing requirements yourself. This forces you to think through what you’re actually trying to build and ensures you understand the problem space.
Once you have your initial requirements, use AI as a research partner. Ask it to expand your understanding of each requirement, explore edge cases, and identify potential challenges. But here’s the crucial part: you must understand and approve every suggestion. You’re the domain expert; AI is your research assistant.
One of AI’s most valuable capabilities in planning is providing a critical perspective. Ask it to challenge your assumptions. Request that it explain what you haven’t justified in your plan. For every suggestion it makes, demand multiple options with pros and cons for each approach. This forces both you and the AI to think more deeply.
Treat planning as an iterative process. Refine your approach based on these conversations. Focus your planning on the “why” behind decisions and touch on the “how” only at the architectural level. Leave implementation details for the implementation phase.
Implementation: Where Plans Meet Reality
Common Mistakes That Waste Time
The most common anti-pattern I see is developers copying requirements from a work item, pasting them into AI, and asking for implementation. It’s the development equivalent of ordering “surprise me” at a restaurant when you’re allergic to seafood.
Accepting AI’s changes without reviewing them is equally dangerous. Yes, the code might run. Yes, the app might seem to work as expected. But “seems to work” isn’t the same as “is correct.” When you discover misalignment later and start going back and forth asking AI to fix issues, you’ve entered a frustrating cycle where you’re treating symptoms instead of addressing root causes.
The Systematic Approach
Successful AI-assisted implementation starts before you ever talk to the AI. Begin by ensuring you have complete understanding of the requirements. If anything is unclear, clarify with stakeholders now—not after AI has generated code based on your incomplete understanding.
Next, prepare comprehensive context. This is your most important step and should include:
- Clear, unambiguous requirements
- Paths to any documents necessary for understanding those requirements (extract relevant sections if documents cover a wider topic than needed, to prevent confusing the AI)
- A specific list of related source code files and folders (and if only certain methods are relevant, list those explicitly so the AI knows where to focus)
With your context prepared, provide it to the AI and ask for an analysis and implementation plan. Do not let the AI change any files yet. This planning phase is separate from execution. Iterate on the plan until you believe all aspects are covered and the plan has clearly separated phases.
Once you’re satisfied, ask the AI to critically evaluate the plan. What could go wrong? What hasn’t been considered? Improve based on valid concerns. Then—and this is crucial—ask the AI to produce a complete implementation plan with proposed source code changes and store it in a local markdown file or your task management tool.
Why store the plan? Context windows have limits. As conversations grow, elements of the original plan can drift or be forgotten. A stored plan serves as your source of truth, allows you to pause and resume work, and enables working on multiple items in parallel.
The Implementation Dance
Now start a fresh AI conversation or clear the context. Point the AI to your stored plan. Implement one phase at a time—no more, no less.
Review each change against your plan. When something is wrong (and it will be sometimes), your response depends on the severity:
- Small mistakes: Fix them yourself or ask the AI to correct them. If asking the AI, stage your current changes first so new iterations are clearly visible.
- Substantial mistakes: Revert your local changes, restart the phase, and include additional clarification to prevent the AI from repeating the error.
Commit your changes after each phase completes. This creates a clear history and provides revert points if needed.
If you discover during implementation that the plan needs adjustment—and you will, because software development is fundamentally about discovery—update your stored implementation plan. Keep it as your single source of truth throughout the process.
The Human Element
The pattern across both planning and implementation is clear: AI is a powerful amplifier of human judgment and knowledge, not a replacement for it. The developers finding the most success with AI aren’t the ones generating the most code—they’re the ones maintaining the highest quality standards while leveraging AI to handle tedious work, explore options, and catch oversights.
Think of AI as an incredibly knowledgeable junior developer who codes quickly but needs clear direction, thorough code review, and can’t be trusted to make architectural decisions independently. Used this way, AI transforms your productivity. Used as a magic solution generator, it transforms your codebase into a maintenance nightmare.
The choice is yours.