Self-corrective Code Generation: A Basic Understanding and Real-life Application

Self-corrective Code Generation is an advanced AI approach where code is not only generated but also continuously refined based on feedback and predefined rules. Unlike traditional methods, this process ensures that code meets essential standards for readability, efficiency, maintainability, and compliance with coding guidelines.

This article will explore how AI-generated code can be incomplete without proper validation, the consequences of using untested code, and how self-corrective code generation solves these challenges. We will also discuss advanced techniques, like multi-step agents, to further enhance code quality, followed by practical examples of how this process works.

1. Sources of Incomplete Code

There are two main sources of incomplete code originating from AI’s output. 

Limited Contextual Information

AI models can only work with the information they are given. When a prompt lacks clarity, specific constraints, or detailed requirements, the model fills the gaps with assumptions. Even when the requests are vague, AI doesn’t ask clarifying questions. It simply generates what seems “most probable”.  

For example, when you ask AI to “prepare test cases for a login function”, AI can generate a list of test cases that may seem technically correct. However, without contextual information, these test cases are incomplete. AI does not include the authentication method your system uses (password-based, OAuth, SSO); what security rules apply (password complexity, rate limiting), etc. At this level, the output only reflects a generic understanding, not the real needs of your system.

One-Pass Generation Constraints

Most AI code generation happens in one direction: the model reads your prompt and immediately returns code. Once the output is generated, the AI doesn’t automatically review it unless you explicitly ask it to. Large language models (LLMs) either do not run or test the code they generate. They rely on patterns from training data to predict what the correct code should look like. As a result, they can produce code that appears valid on the surface but breaks immediately when executed.

For instance, imagine you ask an AI model to “write a function that calculates the total price after applying a discount and tax”. 

def calculate_total(price, discount, tax):
    discounted = price - (price * discount)
    total = discounted + (discounted * tax)
    return total

The code seems fine, but the AI never actually tests it. That means it won’t notice issues like incorrect order of operations (tax might be applied before discount in your business logic). 

2. What Are the Consequences

So, what could be the effects of these sources of incomplete code?

An Increase in Debugging Time

Incomplete or incorrect code forces developers to spend additional time debugging and rewriting the code to meet the required standards. In fact, 67% of developers have to spend more time revising AI’s output (Harness, 2025). This extra time spent on debugging, coupled with the need for a deeper understanding of context, can quickly add up. Gradually, this issue could outweigh the time-saving benefits AI was originally supposed to offer. 

A Likelihood of Hidden Defects Escape from Development

When AI-generated code is not thoroughly checked, there’s a higher likelihood of hidden defects coming to the production environment. According to a study by the IEEE Computer Society, 35% of found bugs are related to incomplete code. As AI tools become more widely adopted in development workflows, this percentage is likely to rise. 

A Reduction in Trust and Adoption of AI Tools

If AI-generated code continues to produce errors, teams may lose trust in AI and move back to the traditional approach. The persistent need for manual corrections and validation could undermine the perceived value of AI. Teams would rely more on their own expertise and established processes rather than embracing AI’s potential for automation and efficiency.

3. Self-corrective Code Generation

To minimize these impacts, testing teams need an approach that gets AI to auto-review its output to polish it again and again before finalizing for testers. That’s when Self-corrective Code Generation comes in. 

What is Self-corrective Code Generation, in Simple

Self-corrective Code Generation refers to the AI testing process in which AI models generate code and then iteratively refine their output based on predefined rules, feedback, or tests. This is the key difference between this approach and the traditional one. This turns AI from code generation tools into smart assistants, which can improve the output before finalizing it with developers.

How does Self-corrective Code Generation Work

1. Question: The process begins with a question. This could be a prompt asking the AI to generate code based on a given task or problem description.
2. Generation (Node): Once the question is inputted, the AI enters the generation phase. 
3. Draft answer: The model generates a response, which is structured as a Pydantic Object. There are three main components: 

• Preamble: a short note to indicate what the code does and why the code is there.
• Imports: a list of tools or libraries needed for the code to function properly is available.
• Code: the actual solution to the problem or task you asked AI for.

4. Import Check (Node): After generating the code, the next step involves checking whether the required imports have been correctly included. This step ensures that the code references all necessary external libraries or modules. If any required imports are missing or incorrectly referenced, the code is flagged as incomplete and fails at this stage. It will go back to the Generation (Node) to edit the Imports section of the Draft answer. 
5. Code Execution Check (Node): Once the imports are verified, the AI moves on to the most critical step: the code execution check. This step involves executing the generated code in a controlled environment to ensure it runs as expected. The logic here is similar to the Import Check (Node): 

• If the code runs without errors, it passes the check.
• If the code fails or encounters an issue, the AI detects the problem and sends the code back to the Generation (Node) to make corrections.

6. Final Answer:  This auto review-and-refine process creates a feedback loop, where the AI continuously checks and improves the code. Only the output that successfully passes all the checks will be finalized and presented as the final solution.

4. Self-corrective Code Generation Using Multistep Agent

An advanced approach to self-corrective code generation uses multi-step agents, which significantly improve the quality of AI-generated code. Unlike traditional methods that review code in a single pass, multi-step agents iterate through multiple stages of feedback and refinement. Here’s how it works:

• Iterative Refinement: The code generation process involves multiple steps. After generating the initial code, the agent evaluates it based on predefined rules (like readability, efficiency, and robustness) and unit tests. For example: 

import yaml
from jinja2 import Template

class IterativeCodeAgent(ToolCallingAgent):
    def __init__(self, prompt_template: str = None, *args, **kwargs):
        """
        Initialize the IterativeCodeAgent. If no custom prompt template is provided, a default one is used.
        """
        # Use a default prompt template or a provided one
        self.run_prompt = prompt_template or self._load_default_prompt()

        super().__init__(*args, **kwargs)

    def _load_default_prompt(self):
        """
        Loads a default prompt template from a file or predefined string.
        Modify this method if you want to use a hardcoded prompt template instead.
        """
        # For this example, the default prompt template is hardcoded.
        # You can replace this with YAML or another dynamic template if needed.
        return """
        You are a helpful code generation assistant. Based on the given instructions, generate the required code.
        Instructions: {{ instructions }}
        """

    def run_agent(self, task_instructions: str, *args) -> None:
        """
        Run the agent with the provided task instructions. It will generate code based on the template and input instructions.
        """
        # Use Jinja2 to render the template with the task instructions
        prompt = Template(self.run_prompt)
        task = prompt.render(instructions=task_instructions)

        # Pass the generated task to the parent class for execution
        super().run(task, *args)

• Code Quality Review: A tool within the agent reviews the code like a "human-like" quality checker, ensuring the code meets key principles such as maintainability, efficiency, and adherence to coding standards. Here is an illustration of how your prompts can be: 

prompt_template = """
You are an AI code reviewer. Your task is to analyze the provided code and ensure it meets the following principles:

1. **Readability**:
  - Check if the code is easy to understand, even by someone who didn't write it. Ensure the use of meaningful variable names, consistent formatting, and proper variable and argument typing.

2. **Maintainability**:
  - Evaluate if the code is easy to modify, update, and debug. It should follow coding standards, avoid overly complex logic, and be modular when required.

3. **Efficiency**:
  - Check if the code uses resources effectively. It should minimize execution time and memory usage.

4. **Robustness**:
  - Ensure that the code handles errors appropriately. Look for the use of try-except blocks for risky code blocks and proper error handling.

5. **PEP-8 Compliance**:
  - Check if the code follows the PEP-8 style guide. This includes proper indentation, line length, naming conventions, and other style guidelines.

### **Tasks**:
1. Ensure the output follows the expected output.
2. For each of the principles listed above, analyze whether the code meets its respective requirements.
3. Request any changes in the provided code as part of your feedback in the comments.
4. Do not assume any external documentation when reviewing the code.
5. Provide a summary at the end of your feedback to gather all suggestions.
6. At the very end, return a boolean value:
  - **True** if all principles returned True.
  - **False** if any of them returned False.

### **Expected output example**:

1. **Readability**:
  - The code uses clear and descriptive names for functions and variables.
  - A type hint is missing for the input parameter of the function `run(input_string):`

2. **Maintainability**:
  - The solution is modularized into several functions.
  - Error checking and consistent structure make it easy to modify or extend functionalities.

3. **Efficiency**:
  - Code has been written with optimal structures.
  - The solution is using efficient Python built-in functions.

4. **Robustness**:
  - The code includes appropriate error handling through type checks and try-except blocks.

5. **PEP-8**:
  - The code follows PEP-8 guidelines: proper indentation, spacing, meaningful names, and line lengths.

### **Summary**:
- **Readability**: Add type hinting in the declaration of function `run(input_string)`. Proposed solution: `run(input_string: str) -> None:`
- **Maintainability**: No changes required.
- **Efficiency**: No changes required.
- **Robustness**: No changes required.
- **PEP-8**: No changes required.

### Final Decision:
**False**
"""

• Automated Feedback Loop: Based on this review, the agent refines the code, improving it iteratively until it meets the required quality criteria. This feedback loop enhances the AI's ability to produce high-quality, reliable code

Final thoughts

Self-corrective code generation empowers developers by ensuring AI-generated code is continuously reviewed and refined. This results in higher quality, more reliable code with minimal manual intervention. As AI tools evolve, these self-corrective systems will become even more integral to development workflows, helping teams produce faster, better results with confidence.