Optimizing Token Usage in OpenAI API by Sending Only Dynamic Content

Below is one efficient approach to reduce token usage for your use case:

Use Fine-Tuning to Implicitly Encode Static Behavior

Rather than sending your static system instruction with every API call, you can fine-tune a model on your desired assistant behavior. This way, the model “learns” to respond with empathy to prayer requests by default. After fine-tuning, you only need to send the dynamic prayer request in each API call, which should help reduce the overall token usage.

Tracking Token Usage

To ensure you’re optimizing your API calls, you can monitor token usage in a couple of ways:

• API Response Data: The API response includes token counts for both input and output, allowing you to track usage in real time.
• Tokenizer Tool: Use OpenAI’s interactive tokenizer tool to estimate the number of tokens for your combined prompts before sending them. This helps in optimizing and reducing redundant token usage.

Summary Example

Below is a conceptual outline of what your setup might look like:

PYTHONCopied
import openai

# Dynamic prayer request (the only part sent in each API call)
dynamic_prayer_request = "Please pray for my family during this hard time."

# Call the fine-tuned model (assumes you've already fine-tuned it)
response = openai.ChatCompletion.create(
    model="your-fine-tuned-model",
    messages=[
        {"role": "user", "content": dynamic_prayer_request}
    ],
    max_tokens=150  # Set this close to your expected response size to avoid extra token usage
)

# Example of tracking token usage from the response
print("Usage:", response['usage'])

Additional Tips

• Batching Requests: If you have multiple requests that do not require immediate responses, consider batching them. This can further optimize throughput and reduce per-request overhead.
• Fine-Tuning Alternatives: If fine-tuning isn’t feasible, another approach is to start a conversation session where the first message sets the system behavior. However, note that session context might not be available across completely separate API calls, so fine-tuning provides a more robust solution.

By fine-tuning your model to internalize the static behavior and tracking token counts via the API response and tokenizer tools, you'll achieve both lower token usage and maintain the appropriate response context for each dynamic prayer request.