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I Threw a 40,000-Line Codebase at GPT 5.4 for Refactoring

Cowrie
Cowrie
Dev @ Bswen

I had a 40,000-line codebase with 30 services tangled together like spaghetti. I needed to redesign the architecture. My expectations were very low.

To my surprise, GPT 5.4 completed it in one shot.

No previous model had been able to handle anything like this for me. This wasn’t a toy example or a tutorial project — it was real production code with complex interdependencies and business logic that had accumulated over years.

Here’s what I learned about making large-scale refactoring work with GPT 5.4.

The Problem That Broke Previous Models

Large codebase refactoring has always been the Achilles’ heel of AI assistants. They’d:

  • Lose context halfway through
  • Make inconsistent decisions across files
  • Miss critical dependencies between services
  • Suggest changes that broke the build

I’d tried this same refactoring task with earlier models. Each time, I’d get partial solutions that created more problems than they solved. The 30 services in my codebase were too intertwined. A change in one rippled through others in ways the models couldn’t track.

GPT 5.4 changed this dynamic entirely.

Why GPT 5.4 Succeeds Where Others Failed

Three capabilities make the difference:

1. Large Context Window

GPT 5.4 can handle up to 1 million tokens. That’s enough to reason about entire codebases, not just individual files. The model maintains architectural coherence across hundreds of files because it can “see” the whole picture.

2. Codex Training

The model was specifically trained to “read and reason about large, complex codebases, plan work alongside engineers.” This isn’t just general intelligence applied to code — it’s specialized capability for software architecture.

3. High Steerability

OpenAI’s documentation emphasizes that GPT 5.4 is “highly steerable and responsive to well-specified prompts.” This matters enormously for refactoring. You can constrain the model’s behavior precisely, preventing the hallucinations and inconsistencies that plagued earlier attempts.

How to Prepare Your Codebase

Before you throw code at GPT 5.4, you need to prepare. The Reddit success story worked because the codebase had “modest documentation” — enough for the model to understand without overwhelming it.

What to Document

+------------------+---------------------------+
| Document Type    | What to Include           |
+------------------+---------------------------+
| Architecture     | Service boundaries,       |
| Overview         | data flow diagrams,       |
|                  | key dependencies          |
+------------------+---------------------------+
| Service Maps     | Which services talk to    |
|                  | which, API contracts,     |
|                  | shared resources          |
+------------------+---------------------------+
| Business Rules   | Domain logic that must    |
|                  | be preserved, validation  |
|                  | rules, edge cases         |
+------------------+---------------------------+
| Coding Standards | Patterns to follow,       |
|                  | naming conventions,       |
|                  | testing requirements      |
+------------------+---------------------------+

You don’t need exhaustive documentation. You need the right documentation. Focus on what a new senior developer would need to understand the architecture in their first week.

The Pre-Refactoring Checklist

  • Document architectural patterns (service boundaries, data flow)
  • Map service dependencies (who calls what, how data moves)
  • Ensure 80%+ test coverage (safety net for changes)
  • Create feature branches (isolate AI-generated changes)
  • Set up CI/CD validation (catch breaks immediately)

I cannot stress the test coverage point enough. GPT 5.4 will generate code. You need automated tests to verify that code does what you expect.

Prompt Engineering for Large Refactoring

The prompt structure matters enormously. Here’s the template that worked for my 40k-line refactoring:

You are a senior software architect specializing in [your domain].
Your task is to refactor a [size] codebase to achieve [specific goal].


Context:
- Current architecture: [brief description]
- Target architecture: [brief description]
- Constraints: [list what cannot change]
- Services affected: [count and names]


Requirements:
- Maintain backward compatibility for [specific components]
- Follow these coding standards: [link or description]
- Preserve these business rules: [list critical rules]
- Test changes using: [your test framework]

The key is specificity. “Refactor this code” produces garbage. “Refactor these 30 services to separate concerns while preserving the authentication flow, payment processing, and user notification systems” produces results.

Structured Tool Use

OpenAI’s documentation recommends explicit instructions for code tasks:

  • Specify when NOT to use certain modes
  • Provide clear examples of expected workflows
  • Require thorough testing for correctness

I found it helpful to add constraints like:

Constraints:
- Do NOT change the public API of the PaymentService
- Do NOT modify database schema
- Do NOT touch authentication logic without explicit approval

Negative constraints prevent the model from “helpfully” breaking things you want to preserve.

The Step-by-Step Workflow

Phase 1: Analysis

Start by asking GPT 5.4 to understand before transforming:

Analyze this codebase structure and identify:
1. Architectural debt and technical issues
2. Services with high coupling
3. Opportunities for improvement
4. Risks of refactoring


Codebase summary:
[paste your documentation/structure]

This phase produces a shared understanding. You validate that the model sees the same problems you see before asking it to fix them.

Phase 2: Design

Request a detailed migration plan:

Based on the analysis, create a migration plan that:
- Addresses each identified issue
- Minimizes risk at each step
- Preserves system stability
- Can be rolled back if needed


For each step, specify:
- Files to change
- Expected impact
- Test requirements
- Rollback procedure

Phase 3: Incremental Implementation

Break the refactoring into service-by-service changes. For each change:

  1. Generate code changes for one service
  2. Run tests immediately
  3. Validate integration points
  4. Commit if green, debug if red

This incremental approach catches problems early. If GPT 5.4 makes a mistake, you find it in one service, not thirty.

Phase 4: Validation

Instruct the model to generate comprehensive tests:

Generate tests for the refactored services:
- Unit tests for each service
- Integration tests for service interactions
- Regression tests for critical user flows
- Edge case tests for business rules

Phase 5: Documentation

After refactoring, have GPT 5.4 update your documentation:

Update the architecture documentation to reflect:
- New service boundaries
- Changed dependencies
- New patterns introduced
- Migration guide for the team

Handling Context Window Limits

Even with GPT 5.4’s massive context window, very large codebases may exceed limits. OpenAI provides a compaction API for this scenario:

# 1) Compact the current window
compacted = client.responses.compact(
    model="gpt-5.2",
    input=long_input_items_array,
)


# 2) Start the next turn with compacted output
next_input = [
    *compacted.output,
    {"type": "message", "role": "user", "content": new_instruction}
]

The compaction preserves the essential context while reducing token count. You can continue the conversation across multiple “turns” without losing the architectural understanding built up in previous messages.

For the 40k-line codebase, I didn’t need compaction. But for larger projects, this is essential.

Common Problems and Solutions

Problem: Context Window Exhausted

Solution: Use the compaction API. Break the refactoring into phases, compacting between each. Summarize completed phases before starting new ones.

Problem: Inconsistent Patterns Across Services

Solution: Establish a pattern library in your prompt. Reference specific examples the model should follow. Review output for uniformity and correct drift early.

Problem: Business Logic Violated

Solution: Provide explicit business rules in prompts. Use examples of correct behavior. Validate changes against your test suite immediately.

Problem: Integration Tests Fail

Solution: Generate integration tests before implementation changes. Mock services appropriately. Roll out changes incrementally.

Problem: Team Skepticism

Solution: Start with a small pilot project. Show the code review process. Document why AI made specific decisions. Build trust through demonstrated reliability.

Model Selection Guide

Not all refactoring tasks need the most powerful model:

+-------------------+---------------------------+------------------+
| Model             | Best For                  | Cost             |
+-------------------+---------------------------+------------------+
| GPT-5.3-Codex     | Complex architectural     | Highest          |
|                   | reasoning, large-scale    |                  |
|                   | redesigns                 |                  |
+-------------------+---------------------------+------------------+
| GPT-5.3-Codex-Max | Maximum reasoning with    | Very High        |
|                   | "Extra High" effort mode  |                  |
|                   | for critical decisions    |                  |
+-------------------+---------------------------+------------------+
| GPT-5.2           | Iterative refactoring,    | Moderate         |
|                   | good speed/capability     |                  |
|                   | balance                   |                  |
+-------------------+---------------------------+------------------+

For my 40k-line structural redesign, I used GPT-5.3-Codex. The investment was worth it — the model completed in one shot what would have taken weeks manually.

What the Community Is Saying

The Reddit thread on GPT 5.4 first impressions is revealing:

“It required structural changes to around 30 services that are complexly intertwined. To my surprise it completed it in one shot.”

“Not exactly a great benchmark but at the very least no model before has been able to handle things like this for me.”

The consistent theme: developers with low expectations are being surprised. Production codebases, not just prototypes. Real architectural complexity, not just toy examples.

The Cost-Benefit Reality

Traditional refactoring for a 40k-line codebase with 30 services: 2-4 weeks of senior developer time.

GPT 5.4 assisted: Days.

The API costs are non-trivial for large contexts, but they’re a fraction of developer hours. The ROI becomes compelling when you factor in:

  • Faster iteration cycles
  • Comprehensive test generation
  • Pattern consistency across services
  • Knowledge capture in prompts

What I’d Do Differently

Looking back on the successful refactoring:

  1. I’d document more upfront — The modest documentation worked, but clearer service boundary docs would have reduced back-and-forth.

  2. I’d generate tests earlier — I waited until implementation was done. Generating tests first would have caught issues faster.

  3. I’d use more negative constraints — I spent time undoing changes the model made to components I wanted preserved. Explicit “do NOT touch X” constraints would have prevented this.

  4. I’d review in smaller batches — Reviewing 30 services of changes was overwhelming. Incremental reviews would have been faster and more thorough.

The Bottom Line

GPT 5.4 represents a genuine paradigm shift for large codebase refactoring. The model’s ability to maintain architectural coherence across hundreds of files while respecting constraints is unprecedented.

But it’s not magic. Success requires:

  • Thoughtful documentation
  • Specific, well-structured prompts
  • Incremental implementation with testing
  • Human oversight at each step

The developer who achieved the 40k-line refactoring in one shot had prepared their codebase. They provided context. They set constraints. They validated results.

That’s the pattern that works. Prepare thoroughly, prompt specifically, validate continuously. GPT 5.4 will do the heavy lifting, but you still need to hold the wheel.

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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