Question

A retail company uses Amazon Bedrock to build a customer service AI assistant. Analysis indicates that 70% of customer inquiries are simple product questions that a smaller model can handle effectively. However, 30% of inquiries are complex return-policy questions requiring advanced reasoning. The company wants to implement a cost-effective model-selection framework that automatically routes customer inquiries to appropriate models according to inquiry complexity. The framework must preserve high customer satisfaction and minimize response latency. Which solution meets these requirements with the **LEAST** implementation effort?

Accepted Answer

B. Use Amazon Bedrock intelligent prompt routing to automatically analyze inquiries. Route simple product inquiries to smaller models, and route complex return policy inquiries to more capable larger models.

Answer

Create a multi-stage architecture that uses a small foundation model (FM) to classify the complexity of each inquiry. Route simple inquiries to a smaller, more cost-effective model. Route complex inquiries to a larger, more capable model. Use AWS Lambda functions to handle the routing logic.

Answer

Implement a single-model solution that uses an Amazon Bedrock mid-sized foundation model (FM) with on-demand pricing. Include special instructions in model prompts to handle both simple and complex inquiries by using the same model.

Answer

Create separate Amazon Bedrock endpoints for simple and complex inquiries. Implement a rule-based routing system based on keyword detection. Use on-demand pricing for the smaller model and provisioned throughput for the larger model.