[Python] Managing Token Limits in GPT-4o for Variable-Length Input and Output

I’m using GPT-4o via the Azure OpenAI API to handle variable-length queries, but I frequently encounter token exhaustion when the combined token count of my input and output exceeds the model's maximum limit (e.g., 8,192 tokens).

Since the length of responses can vary significantly depending on the complexity of the question, how can I dynamically manage input and output tokens to ensure my requests do not fail?

What’s the best approach to:

Dynamically calculate and reserve tokens for output based on input length? Handle long outputs that cannot fit in a single response? Here’s an example scenario:

Input question: "Describe how each product or investment strategy might be affected by the transition to a low-carbon economy of Hilton." Expected output: A detailed response covering multiple strategies, which may exceed the token limit. I'm currently using the Python SDK. Any advice or code examples would be greatly appreciated!

Set a Static max_tokens:

I tried setting a static value for max_tokens (e.g., 2,000), but this caused issues when the input prompt was too long, leaving insufficient room for the output. Chunked the Input:

I tried breaking down longer questions into smaller queries manually, but this isn't practical for dynamic queries or real-time systems. Monitored Token Usage:

I used token counting tools to calculate the input token length but struggled with dynamically adjusting the output token limit based on the input. Tried Continuation Prompts:

For long responses, I added follow-up queries like "Continue from where you left off," but this approach led to incomplete or redundant outputs.

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