AI Assisted Coding: Code Rust and the Legacy Loop Trap

Today we are going to look at hard-won insights into coding with GPT4o.? Developers hoping to achieve success in today’s market are already skilled, or better get up to speed touts de suite, on 4o’s code assist.? I know.? I know. There are competing technologies.? This article focuses on just the one: GPT4o.?

The topic is, how do we get GPT4o code assist technology to use versions of packages and tools developed AFTER the LLM was deployed and/or trained?? How do we account for new features, changes, and deprecations in our SDKs, APIs, NuGet packages, modules, etc?

What Problem are We Fixing?

Developers using GPT4o to help code frequently run into a problem: Code Rust. Code rust is the limitation where an LLM provides code solutions on outdated or deprecated versions of APIs, SDKs, GitHub/npm/etc.? Emergent technology tools like Azure.AI.OpenAI move fast.? New capabilities, changes to functional usage, return values, and updated calling footprints are frequently introduced.? Sometimes daily…at least it feels that way.? Deprecation occurs.? Deprecated functions in fielded code obsoletes entire capability sets.?

Unmanaged code rust results in solutions built on tools leapfrogged by the market at deployment.? Day one begins with version lock. Fixes and modifications are expensive and difficult.?

Managed code rust, while better, carries higher upfront costs.? Code must be compiled, exceptions/errors captured and accounted for, often by feeding them back into 4o.? If the LLM is significantly outdated, it is unable to correct issues from its own code. At best, GPT4o suggests a workaround that addresses excepted code by deviating from best practices.? At worst, the user then researches the exception and fixes the code. ?On-the-fly refactoring of your own code is rife with opportunities for error, updating generated code exponentially more so.?

Legacy Loops are a frustrating occurrence where an LLM like GPT4o generates code based on outdated package definitions. On compilation, the code has exceptions or functional discontinuities.? The developer then feeds errors and issues back to the LLM for correction. GPT4o does not contain the latest specs so a fix is unavailable.? At this point, GPT4o takes an educated guess at repairing the code. Sometimes it guesses correctly. Sometimes. Often, however, the update introduces new errors or does not fix bad code. Developers redeploy and compile, leading to new errors.? Another feedback loop starts. But the LLM does not perform incremental improvements. Instead, it recognizes the inferiority of the last code fix and reproduces identical code to the initial, broken set.? Cue old-time merry-go-round music. How nice.? An infinitely self-referencing exception loop.? Progress is stalled until the user fixes the issue by either manually debugging or rephrasing the LLM requirement.? Frustration ensues.

Solving Code Rust / Legacy Loops

Back to the problem at hand.? How do we get GPT4o up to speed on the newest, shiniest, new software.? If the model release cycle is four or six months, we could be waiting longer than market interest in our product remains “hot”.

Step 1: Provide Updated Documentation and Release Notes

Include Links or Summarize Changes:

• When prompting GPT4o, provide links to the official documentation and release notes of the new SDK version. If possible, summarize the key changes, new features, and deprecated functions.

Example Prompt:

The Azure.AI.OpenAI SDK has been updated to version 2.0. Here is the link to the latest documentation: Azure OpenAI client library for .NET - Azure for .NET Developers | Microsoft Learn.

The key changes in this version include:
- New feature: XYZ functionality.
- Deprecation: ABC method is deprecated.
- Changes: The method signature for DEF has been updated to include an additional parameter.

Please use this information to write code using the latest SDK version.        

Step 2: Specify the SDK Version in Your Environment

Use the Latest SDK in Your Project:

• Ensure your development environment and project are using the latest version of the SDK. This will help GPT4o to generate code that is compatible with the newest version.

Example:

dotnet add package Azure.AI.OpenAI --version 2.0.0        

Step 3: Write Detailed Prompts

Include Specific Requirements and Context:

• Write detailed prompts that include specific requirements and context for the task you want to accomplish. This helps GPT4o to generate accurate and relevant code.

Example Prompt:

Using the latest version of Azure.AI.OpenAI SDK (version 2.0), write a C# program that initializes the OpenAI client, sends a text completion request using the new XYZ feature, and handles the response. Refer to the updated method signatures and ensure that deprecated methods are not used.        

Step 4: Use GPT4o for Incremental Code Generation

Generate Code in Steps:

• Use GPT4o to generate code incrementally. Start with initializing the client, then move on to specific features, and finally, handle responses and errors.

Example:

1. Initialize the Client:

Write a C# function to initialize the OpenAI client using the latest Azure.AI.OpenAI SDK.        

using Azure;
using Azure.AI.OpenAI;

public class OpenAIService
{
    private OpenAIClient _client;
    public OpenAIService(string apiKey)
    {
        _client = new OpenAIClient(new Uri("https://api.openai.com/"), new AzureKeyCredential(apiKey));
    }
}        

2. Send a Text Completion Request:

Extend the OpenAIService class to include a method that sends a text completion request using the new XYZ feature of the latest SDK.        

public async Task<string> GetTextCompletion(string prompt)
{
    var requestOptions = new TextCompletionRequestOptions
    {
        Prompt = prompt,
        MaxTokens = 50,
        NewXYZParameter = "value" // Example of a new feature parameter
    };
    Response<TextCompletionResponse> response = await _client.Completions.CreateCompletionAsync(requestOptions);
    return response.Value.Choices.FirstOrDefault()?.Text;
}        

3. Handle Responses and Errors:

Add error handling to the GetTextCompletion method to manage any potential issues using the latest Azure.AI.OpenAI SDK conventions.        

public async Task<string> GetTextCompletion(string prompt)
{
    try
    {
        var requestOptions = new TextCompletionRequestOptions
        {
            Prompt = prompt,
            MaxTokens = 50,
            NewXYZParameter = "value" // Example of a new feature parameter
        };
        Response<TextCompletionResponse> response = await _client.Completions.CreateCompletionAsync(requestOptions);

        return response.Value.Choices.FirstOrDefault()?.Text;
    }
    catch (RequestFailedException ex)
    {
        Console.WriteLine($"Error: {ex.Message}");
        return null;
    }
}        

Step 5: Verify and Test Generated Code

Test in Your Environment:

• Run and test the generated code in your local development environment to ensure it works as expected with the latest SDK version.

Step 6: Provide Feedback to GPT4o

Iterate Based on Output:

• If the initial code generated by GPT4o is not fully compatible, provide feedback and iterate on the code generation process.

Example Feedback:

The generated code is using a deprecated method. Please update the code to use the new method NewCompletionMethod introduced in version 2.0 of the Azure.AI.OpenAI SDK.        

Secret Tips

1. Update GPT4o with Documentation Links: Always provide GPT4o with the latest documentation links for the SDK you are using. This helps GPT4o access the most current information.
2. Incremental Prompts: Break down your requests into smaller, incremental prompts. This ensures that GPT4o can focus on one aspect of the code at a time and reduces the likelihood of errors.
3. Manual Review and Adjustments: After generating code, manually review and adjust it as necessary. GPT4o is a powerful tool, but human oversight ensures that the code meets your exact requirements.
4. Combine Multiple Prompts: Use multiple prompts to refine and enhance the code. For example, start with generating the basic structure, then add error handling, and finally, optimize the code.

By following these steps and tips, you can effectively use OpenAI GPT4o to write programs that are up to date with the latest SDK versions, even if the LLM was trained on earlier package implementations.? Even if the package is new and the model has no knowledge of it. It may seem a convoluted, but the changes to an AI assisted developer flow are minimal.? Benefits start accruing the first time you hit code rust.