code_fixing_and_diff.md

# [Code Fixing and Diff Management in GPT-4.1](https://chatgpt.com/canvas/shared/6825f21e65388191b9fb0baa737c1f18)

## Overview

This document provides a comprehensive implementation guide for code fixing and diff generation strategies using the OpenAI GPT-4.1 model. It is designed to help developers and tool builders harness the model’s improved agentic behavior, tool integration, and patch application capabilities. The guidance herein is based on OpenAI’s internal agentic workflows, as tested on SWE-bench Verified and related coding benchmarks.


## Objectives

* Enable GPT-4.1 to autonomously fix software bugs with minimal user intervention
* Standardize high-performance diff formats that GPT-4.1 understands well
* Leverage tool-calling strategies that minimize hallucination and improve precision
* Scaffold workflows for validation, patch application, and iterative debugging


## Core Principles for Effective Bug Fixing

### 1. Persistent Multi-Step Execution

To prevent premature termination, always instruct the model to:

```text
Continue working until the issue is fully resolved. Do not return control to the user unless the fix is complete and validated.
```

This aligns GPT-4.1’s behavior with full agent-mode operation.

### 2. Tool-Use Encouragement

Rather than letting the model hallucinate file contents:

```text
Use your tools to examine the file system or source code. Never guess.
```

This ensures queries are grounded in actual project state.

### 3. Planning and Reflection Enforcement

Prompt the model to:

* Plan before tool calls
* Reflect after each execution
* Avoid chains of back-to-back tool calls without synthesis in between

**Prompt Template:**

```text
You MUST plan extensively before calling a function, and reflect thoroughly on its output before deciding your next step.
```


## Workflow Structure

### High-Level Task Phases

1. **Understand the Bug**
2. **Explore the Codebase**
3. **Plan the Fix**
4. **Edit the Code**
5. **Debug and Test**
6. **Reflect and Finalize**

Each of these phases should be scaffolded in the prompt or system instructions.

### Recommended Prompt Structure

```markdown
# Instructions
- Fix the bug completely before ending.
- Use available tools.
- Think step-by-step before and after each action.

# Workflow
1. Understand the issue.
2. Investigate the source files.
3. Plan an incremental fix.
4. Apply and validate patch.
5. Test extensively.
6. Reflect and iterate.
```


## The V4A Patch Format (Recommended)

GPT-4.1 performs best with this clear, human-readable patch format:

```bash
*** Begin Patch
*** Update File: path/to/file.py
@@ def some_function():
 context_before
-    buggy_code()
+    fixed_code()
 context_after
*** End Patch
```

### Diff Format Rules

* Use `*** Update File:` to mark the file.
* Use `@@` to denote function or class scope.
* Precede old code lines with `-`, new code with `+`.
* Include 3 lines of context above and below the change.
* If needed, add nested `@@` scopes for disambiguation.

**Avoid line numbers**; GPT-4.1 does not rely on them. It uses code context instead.


## Tool Configuration: `apply_patch`

To simulate developer workflows, define a function tool with this pattern:

```json
{
  "name": "apply_patch",
  "description": "Apply V4A diff patches to source files",
  "parameters": {
    "type": "object",
    "properties": {
      "input": { "type": "string" }
    },
    "required": ["input"]
  }
}
```

**Input Example:**

```bash
%%bash
apply_patch <<"EOF"
*** Begin Patch
*** Update File: mymodule/core.py
@@ def validate():
-    return False
+    return True
*** End Patch
EOF
```

The `apply_patch` tool accepts multi-file patches. Each file must be preceded by its action (`Add`, `Update`, or `Delete`).


## Testing Strategy

### Manual Testing within Prompt:

Prompt the model to run tests after every change:

```text
Run all unit tests using `!python3 run_tests.py`. Do not assume success without verification.
```

### Encourage Reflection:

```text
Did the test results indicate success? Were any edge cases missed? Do you need to write new tests?
```

### Output Evaluation:

* If tests fail, model should explain why and iterate
* If tests pass, model should reflect before finalizing


## Debugging and Investigation Techniques

### Investigation Plan Example:

```text
I will begin by reading the test file that triggered the error, then locate the corresponding implementation file. From there, I’ll trace the logic and verify any assumptions.
```

### Debugging Prompt Reminders:

* Never change code without full context
* Use tools to inspect contents before editing
* Print debug output if necessary


## Failure Mode Mitigations

| Failure Mode                 | Fix Strategy                                                            |
| ---------------------------- | ----------------------------------------------------------------------- |
| Patch applied in wrong place | Add more surrounding context or use double `@@` scope                   |
| Patch fails silently         | Check patch syntax and apply logs before "Done!" line                   |
| Model ends before testing    | Insert reminder: "Do not conclude until all tests are validated."       |
| Partial bug fixes            | Require model to re-verify against original issue and user expectations |


## Final Validation Phase

Before finalizing a solution, prompt the model to:

* Re-read the original problem description
* Confirm alignment between intent and fix
* Run a fresh test suite
* Draft additional tests for uncovered scenarios
* Watch for silent failures or fragile patches

### Final Prompt Template:

```text
Think about the original bug and the goal. Is your fix logically complete? Did you run all tests? Are hidden edge cases covered?
```


## Alternative Diff Formats

If you need variations, GPT-4.1 performs well with:

### Search/Replace Format

```text
path/to/file.py
>>>>>> SEARCH
def broken():
    pass
=======
def broken():
    raise Exception("Fix me")
<<<<<< REPLACE
```

### Pseudo-XML Format

```xml
<edit>
  <file>path/to/file.py</file>
  <old_code>def old(): pass</old_code>
  <new_code>def old(): raise NotImplementedError()</new_code>
</edit>
```

These are most useful in pipeline or IDE-integrated settings.


## Best Practices Summary

| Principle                 | Practice                                               |
| ------------------------- | ------------------------------------------------------ |
| Persistent Agent Behavior | Model must keep going until the fix is verified        |
| Reflection                | Insert plan-and-reflect instructions at each phase     |
| Patch Format              | Use V4A or equivalent context-driven diff structure    |
| Testing                   | Prompt to test after every step                        |
| Finalization              | Always include a validation + extra test writing phase |


## Conclusion

GPT-4.1 can serve as a robust code-fixing agent when scaffolded with precise patch formats, rigorous test validation, and persistent reflection mechanisms. By integrating tool calls such as `apply_patch` and emphasizing validation over completion, developers can reliably use the model for end-to-end issue resolution workflows.

**Build the fix. Test the outcome. Validate the solution.** That’s the foundation for agentic software repair with GPT-4.1.