TLDR: If you’re getting a 400 Bad Request error with Azure OpenAI’s ChatCompletion API, and there’s no useful error message, try adding custom logging and retry policies to your client setup.

Here’s a quick fix using a custom PipelinePolicy to log detailed request/response information:

Custom Logging Middleware for Better Error Visibility

public class CustomLoggingPolicy : PipelinePolicy
{
    public override void Process(PipelineMessage message, IReadOnlyList<PipelinePolicy> pipeline, int currentIndex)
    {
        ProcessNext(message, pipeline, currentIndex);
    }

    public override async ValueTask ProcessAsync(PipelineMessage message, IReadOnlyList<PipelinePolicy> pipeline, int currentIndex)
    {
        Console.WriteLine($"Sending Request to {message.Request.Uri}");

        await ProcessNextAsync(message, pipeline, currentIndex);

        if (message.Response?.IsError == true)
        {
            Console.WriteLine($"Request to {message.Request.Uri}, " +
                $"ReasonPhrase: {message.Response.ReasonPhrase} " +
                $"ResponseContent: {message.Response.Content}");
        }
    }
}

Custom Retry Policy for Rate Limiting

public class CustomClientRetryPolicy : ClientRetryPolicy
{
    protected override async ValueTask<bool> ShouldRetryAsync(PipelineMessage message, Exception? exception)
    {
        if (message.Response.Status == (int)HttpStatusCode.TooManyRequests)
        {
            return false;
        }
        return await base.ShouldRetryAsync(message, exception);
    }
}

Apply Policies to AzureOpenAIClientOptions

AzureOpenAIClientOptions clientOptions = new();
clientOptions.AddPolicy(new CustomLoggingPolicy(), PipelinePosition.BeforeTransport);
clientOptions.RetryPolicy = new CustomClientRetryPolicy();

AzureOpenAIClient azureOpenAiClient = new(
    new Uri("https://your-openai-instance.openai.azure.com/"),
    new DefaultAzureCredential(),
    clientOptions
);

This should help surface useful error messages and prevent excessive retries when encountering rate limits.

References:

The Journey: Debugging OpenAI 400 Errors

My Specific Issue: Rate Limits (But I Tried Everything First)

When I first encountered the 400 Bad Request error in Azure OpenAI’s ChatCompletion API, I had no clue what was wrong. The response contained zero useful details, which made debugging frustrating.

before

At first, I considered multiple possible causes and tested each one before finally discovering that rate limits were the real issue. Here’s how I approached debugging:

  1. Checked API Key and Authentication ✅
    • Used ApiKeyCredential to ensure I wasn’t using incorrect credentials.
    • Verified that my deployment name matched what I set up in Azure OpenAI.
  2. Validated Request Payload ✅
    • Manually tested different parameters (MaxTokens, Temperature, TopP) to see if invalid values were causing the issue.
    • Used Azure’s AI Studio (ai.azure.com) sample code to compare it to my code. sample code
  3. Suspected Rate Limits (Bingo! 🎯)
    • After exhausting all other options, I started logging request details.
    • Turns out, Azure sometimes returns 400 instead of 429 for rate limits!
    • Implementing custom logging and retry policies (as shown earlier) finally helped identify the root cause.

after

Debugging Journey: Searching for the Solution

I searched through the README, official documentation, and wiki articles, but they didn’t provide a clear answer. Eventually, I stumbled upon a GitHub issue that described symptoms similar to mine.

That’s when I found this comment, which proposed adding a custom logging policy to reveal more details about the API response. This helped me confirm the issue was related to rate limiting.

Lessons Learned

  • Always log request/response details to debug issues.
  • Rate limits can return misleading errors—watch for 400 that might be 429.
  • Azure OpenAI SDK lacks detailed error handling—adding a Custom Logging Policy helps.
  • Check AI Studio (ai.azure.com) for payload validation before coding.

Future Improvements

  • Automate rate limit handling with dynamic backoff instead of outright failing on 429 errors.
  • Improve error messages in SDK (maybe even contribute to the official repo 🤔).
  • Experiment with batching requests to reduce token consumption.

Final Thoughts

Debugging Azure OpenAI’s 400 errors was frustrating, but this journey reinforced a crucial lesson:

📌 When APIs don’t give you useful error messages, build your own debugging tools.

If you’re struggling with similar issues, try implementing a Custom Logging & Retry Policy—it made a world of difference for me.

If you’ve faced weird OpenAI API issues, I’d love to hear about them.

🔗 Related Links

What Do You Think?

Let’s chat! Have you run into weird OpenAI errors? What debugging tricks worked for you? 🚀