Debugging issues with OpenAI API calls involves systematically checking for common errors, validating your inputs, and using available tools to diagnose problems. Start by examining the HTTP status code and error message returned by the API. For example, a 401 Unauthorized error typically indicates an invalid or missing API key, while a 400 Bad Request often points to incorrect parameters or malformed requests. The API response usually includes a specific error code (e.g., invalid_api_key or rate_limit_exceeded) that helps narrow down the issue. Tools like Postman or command-line utilities like curl can help isolate problems by testing requests outside your application code. Always verify that your API key is correctly set, your request headers (like Content-Type: application/json) are properly configured, and your network isn’t blocking outgoing requests.
Next, validate your input data and parameters. For instance, if you’re using the chat/completions endpoint, ensure your messages array follows the required structure—each message must have a role (e.g., “user”) and content field. Incorrect parameter values, such as a temperature set to 2.0 (outside the valid 0-2 range), will trigger errors. If the API returns unexpected output, test with simpler prompts to rule out formatting issues. For example, a malformed JSON payload (like a missing comma or quote) will cause a parsing error. Use a JSON validator or linter to check your request body. Additionally, some errors stem from token limits: if your input exceeds the model’s maximum context length (e.g., 4096 tokens for older GPT-3 models), truncate or shorten the text.
Finally, use logging and tracing to diagnose intermittent or complex issues. Log the full request and response details (masking sensitive data like API keys) to review inputs and outputs. If the API returns a vague error, check OpenAI’s documentation for known issues or service status updates. For timeouts or slow responses, verify your network latency or retry with exponential backoff. Tools like the OpenAI Playground can help test parameters in a UI before integrating them into code. If you’re using a library like openai-python, ensure it’s updated—outdated versions might have compatibility issues. For persistent problems, share a minimal reproducible example in community forums or OpenAI support, including the exact error message, code snippet, and model used (e.g., gpt-4 vs. gpt-3.5-turbo).