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How Do I Iterate with AI Until It Mirrors My Project Vision Back to Me?

Cowrie
Cowrie
Dev @ Bswen

I kept getting generic, compromised solutions from AI coding assistants. Every time I asked for help, I’d get something that “worked” but didn’t match what I actually wanted. The code would run, but it wasn’t my vision.

Here’s what I realized: the AI’s first response is always contaminated by its default assumptions about who I am and what I need.

The Hidden Problem: Baked-In Assumptions

When you prompt an AI coding assistant, it doesn’t start from a blank slate. It starts with assumptions:

┌─────────────────────────────────────────────────┐
│           AI's Default Mental Model              │
├─────────────────────────────────────────────────┤
│  - You're working solo                          │
│  - You have limited time                        │
│  - You have no existing infrastructure           │
│  - You need "practical" compromises             │
│  - You want the fastest solution, not the best  │
└─────────────────────────────────────────────────┘

These assumptions are invisible but powerful. They shape every suggestion, every code block, every architectural decision. The AI gives you what it thinks you need — not what you actually envision.

I’d ask for a REST API and get a simple CRUD implementation. But I already had that — I needed role-based access control layered on top, with audit logging in a separate data store. The AI didn’t know that. It assumed I was starting from zero.

The Solution: Iterative Pushback Protocol

The real work happens after the first response. Here’s the workflow that changed everything:

┌──────────────┐     ┌──────────────┐     ┌──────────────┐
│   Your       │────▶│   AI's       │────▶│   Your       │
│   Vision     │     │   Response   │     │   Correction │
└──────────────┘     └──────────────┘     └──────────────┘
                                                 │
                                                 ▼
       ┌─────────────────────────────────────────────────┐
       │                                                  │
       ▼                                                  │
┌──────────────┐     ┌──────────────┐     ┌──────────────┐ │
│   Refined    │────▶│   AI's       │────▶│   More       │ │
│   AI View    │     │   Restatement│     │   Corrections│ │
└──────────────┘     └──────────────┘     └──────────────┘ │
       ▲                                                  │
       │                                                  │
       └──────────────────────────────────────────────────┘
                          (repeat until clean)
                                 │
                                 ▼
                     ┌───────────────────┐
                     │   Vision Locked   │
                     │   (no corrections │
                     │    needed)        │
                     └───────────────────┘

The iteration step IS the whole game. You keep pushing back until the AI can duplicate your ideal vision back to you without correction. When you stop correcting, the vision is locked.

How It Actually Works in Practice

Here’s a real conversation flow:

Me: I want a REST API for user management.

AI: Got it — a simple CRUD API with endpoints for create, read, update, delete users.

Me: No, I already have that. I need role-based access control with audit logging.

AI: Understood — building on existing CRUD, adding RBAC and audit trails. Should the audit log be immutable?

Me: Yes, append-only, stored separately from the main DB.

AI: Let me restate: You have existing user CRUD. You need RBAC layered on top, with append-only audit logs in a separate data store. The audit should capture who changed what, when.

Me: [No corrections needed]

AI: Vision locked. Proceeding with implementation plan…

Notice the key step: I asked for restatement. The AI described my vision back to me, and I verified it was correct. Only then did we proceed.

The Prompt Template I Use Now

Here’s a reusable template for vision alignment sessions:

I'm going to describe a project vision. Your job is to:
1. Ask clarifying questions
2. Restate the vision in your own words
3. I'll correct anything that's wrong
4. We repeat until your restatement is perfect


My vision: [describe project]


---


After alignment, output:
## Locked Vision
[Your confirmed understanding]


## Saved Insights
- [Key decision 1 and why]
- [Key decision 2 and why]
Rate each insight 1-5 for importance.

The “Saved Insights” section is crucial. When I get stuck later in development, I can say:

Check saved insights before proposing solutions.

This reminds the AI to use our established context rather than defaulting to generic suggestions.

Why This Matters More Than “Better Prompts”

Most advice about AI coding assistants focuses on writing better prompts. But prompts are just the starting point. The real work happens in the iteration:

  • Shared mental model: You’re not guessing what the AI will do; you know it understands
  • Reduced rework: Alignment upfront prevents wrong-direction implementation
  • Better solutions: The AI can build on your vision instead of working around its assumptions
  • Confidence: You trust the AI’s output because you’ve verified its understanding

The AI takes your rough ideas and cleans up the edges, iterating toward something better than your original plan. But only if you push back.

Common Mistakes I Made

  1. Accepting the first response as “good enough” — It never is. The first response is contaminated by assumptions.

  2. Not being specific about what’s wrong — “That’s not right” doesn’t help. “I already have a CI/CD pipeline, don’t suggest setting one up” is specific and actionable.

  3. Skipping the “describe it back to me” step — Without verification, you’re still trusting a black box.

  4. Forgetting to save insights — Each session becomes a fresh start, losing all the context you built.

A Simple ASCII Diagram of the Difference

Without iterative pushback:

Your Vision ──▶ [Black Box AI] ──▶ Compromised Output
                                                    │
                                                    ▼
                                          You accept or reject
                                          (still guessing)

With iterative pushback:

Your Vision ──▶ [AI Interpretation] ──▶ Your Correction ─┐
    ▲                                                   │
    │                                                   ▼
    │                              [Refined Interpretation]
    │                                                   │
    └─────────────────── Your Verification ◀───────────┘
                                   │
                                   ▼
                          [Locked Vision] ──▶ Implementation
                                   │
                                   ▼
                         You know it's right

What Changed for Me

After adopting this approach, my AI-assisted development workflow transformed. Instead of constantly fixing AI-generated code that didn’t match my vision, I spent more time upfront aligning. The result:

  • Fewer iterations on implementation
  • Less frustration with “almost right” solutions
  • More confidence that the AI understood my context
  • Reusable insights I could reference in future sessions

The AI isn’t a magic solution generator. It’s a collaborator who needs to earn your trust by demonstrating it understands. Push back until the AI can describe your vision without correction; then you’re no longer trusting a black box, you’re working with a partner who shares your mental model.

Final Words + More Resources

My intention with this article was to help others share my knowledge and experience. If you want to contact me, you can contact by email: Email me

Here are also the most important links from this article along with some further resources that will help you in this scope:

Oh, and if you found these resources useful, don’t forget to support me by starring the repo on GitHub!

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