Question
An ecommerce company is developing a generative AI (GenAI) solution that uses Amazon Bedrock with Anthropic Claude to recommend products to customers. Customers report that some of the recommended products are not available for sale on the website or are not relevant to the customer. Customers also report that the solutions takes a long time to generate some recommendations.
The company investigates the issues and finds that most interactions between customers and the product recommendation solution are unique. The company confirms that the solutions recommends products that are not in the company's product catalog. The company must resolve these issues.
Which solution will meet this requirement?
- A. Increase grounding within Amazon Bedrock Guardrails. Enable Automated Reasoning checks. Set up provisioned throughput.
- B. Use prompt engineering to restrict the model responses to relevant products. Use streaming techniques such as the InvokeModelWithResponseStream action to reduce perceived latency for the customers.
- C. Create an Amazon Bedrock knowledge base. Implement Retrieval Augmented Generation (RAG). Set the PerformanceConfigLatency parameter to optimized. Correct
- D. Store product catalog data in Amazon OpenSearch Service. Validate the model's product recommendations against the product catalog. Use Amazon DynamoDB to implement response caching.
Explanation
Option C is correct because creating an Amazon Bedrock knowledge base and implementing Retrieval Augmented Generation (RAG) ensures that the GenAI solution can reference up-to-date and relevant information from the company's product catalog, addressing the issue of recommending unavailable or irrelevant products. Setting the PerformanceConfigLatency parameter to optimized helps in reducing the latency issues experienced by customers.
