Why AI Struggles with Iterative Coding—and How to Overcome It

As someone who has worked with AI tools extensively, I've noticed an interesting pattern: AI can be brilliant at "one-shot" coding. When you ask it to create something from scratch—be it a function, a script, or even a complex feature—it often nails the task. But the moment you ask it to revisit or refine its own work in a later session, things can start to unravel.

Why does this happen? Let’s explore—and more importantly, let’s talk about how we can bridge this gap.

The Challenges

1. Context Loss: AI doesn’t retain memory across sessions unless you explicitly provide context. Unlike a human coder who remembers why certain decisions were made, AI needs to be reminded—constantly.
2. Understanding Its Own Code: AI can generate code that works, but asking it to deeply analyze and modify that code later can be a tall order. It doesn’t inherently “know” the logic it created; it only processes what it’s given at the moment.
3. Incremental Changes Are Hard: Sometimes, you don’t need a new feature; you need a small tweak or bug fix. This requires understanding the entire system—a challenge for an AI working without full context.
4. Dependencies and State Management: Real-world projects aren’t isolated scripts; they involve dependencies, state, and evolving requirements. Without continuity, AI often struggles to keep track of these moving pieces.

Solutions: Making AI Work Like a Team Player

As a business owner and software engineer, I’ve been refining ways to make AI tools more effective, especially when working on iterative development. Here’s what has worked for me:

1. Provide Detailed Context Every Time

AI performs best when you set it up for success. Share not just the code snippet you want to edit but also any relevant comments, dependencies, and goals. Treat it like onboarding a new developer.

2. Break Projects into Modular Components

Smaller, self-contained modules are easier for AI to process. Modularization isn’t just good practice for humans; it helps AI, too.

3. Persist Context Across Sessions

One game-changer is maintaining a “project overview” document. This acts as the memory AI doesn’t have, summarizing the structure, goals, and any previous changes. Share this document with the AI at the start of every session.

4. Ask for Reviews Before Edits

Instead of diving straight into changes, I’ve started asking AI to review its own code first. This approach lets the AI identify potential issues or dependencies before making changes.

5. Use Comments and Version Control Wisely

Encourage AI to add comments when it generates code. Tools like Git can provide a history of changes, offering context for both humans and AI in future sessions.

6. Leverage Testing and Automation

Linting and automated testing frameworks have been invaluable. After the AI modifies code, these tools can quickly identify inconsistencies or bugs, saving time.

7. Experiment with Fine-Tuned Models

If you’re working on a long-term project, consider fine-tuning AI on your specific style, domain knowledge, and project conventions. This reduces friction when AI re-engages with the code later.

8. Treat AI as a Partner, Not a Replacement

AI shines brightest when paired with human oversight. Think of it as a junior developer: great at generating ideas but still needing guidance for refinement and iteration.

The Future of AI in Software Development

AI is evolving rapidly, and these limitations are just bumps on the road. The real magic lies in combining AI’s raw problem-solving power with human creativity and judgment. By developing workflows that play to AI’s strengths and account for its weaknesses, we can unlock even greater potential.

As someone passionate about technology and its ability to empower businesses, I’m excited about where we’re headed. But I’m curious—what challenges have you faced when working with AI on iterative coding? And how are you solving them? Let’s share insights and grow together!

This is a topic I care deeply about, and I’d love to hear your thoughts. Drop a comment or send me a message if you’re working on similar challenges—I’m always up for a good conversation about the future of AI in software development.