Decreasing the number of tokens in the prompt reduces the cost associated with using an LLM model on Amazon Bedrock, as costs are often based on the number of tokens processed by the model.
Token Reduction Strategy:
By decreasing the number of tokens (words or characters) in each prompt, the company reduces the computational load and, therefore, the cost associated with invoking the model.
Since the model is performing well with few-shot prompting, reducing token usage without sacrificing performance can lower monthly costs.
Why Option B is Correct:
Cost Efficiency: Directly reduces the number of tokens processed, lowering costs without requiring additional adjustments.
Maintaining Performance: If the model is already performing well, a reduction in tokens should not significantly impact its performance.
Why Other Options are Incorrect:
A. Fine-tuning: Can be costly and time-consuming and is not needed if the current model is already performing well.
C. Increase the number of tokens: Would increase costs, not lower them.
D. Use Provisioned Throughput: Is unrelated to token costs and applies more to read/write capacity in databases.
