An ecommerce company is developing a generative AI application that uses Amazon Bedrock with Anthropic Claude to recommend products to customers…

The core issues are hallucination (recommending unavailable products) and high perceived latency. The most effective solution for grounding a model in factual, up-to-date information is Retrieval Augmented Generation (RAG). Creating an Amazon Bedrock knowledge base with the company's product catalog and implementing RAG ensures the model's responses are based only on available products, directly resolving the relevance and availability issues. While the PerformanceConfigLatency parameter is not a real API parameter, the intent of the option points towards optimizing the RAG process, which is the correct architectural pattern to solve the primary problem.

A. Guardrails are for enforcing safety policies, not for grounding responses in a product catalog. Provisioned throughput addresses consistent load, not initial generation latency or hallucination.
B. Prompt engineering alone is often insufficient to prevent hallucinations with large, dynamic datasets like a product catalog. Streaming reduces perceived latency but does not fix the grounding problem.
D. Post-generation validation adds latency and complexity. Caching is ineffective because most interactions are unique, as stated in the problem description.

1. AWS Documentation. (2024). Knowledge bases for Amazon Bedrock. AWS. Retrieved from https://docs.aws.amazon.com/bedrock/latest/userguide/knowledge-base.html. (This explains how knowledge bases provide models with access to data sources for RAG, which is used to "deliver more relevant and accurate responses").

2. AWS Documentation. (2024). Retrieval Augmented Generation (RAG). AWS. Retrieved from https://docs.aws.amazon.com/sagemaker/latest/dg/jumpstart-foundation-models-rag.html. (This page describes RAG as a technique to reduce hallucination by providing external, verifiable knowledge to the model).

3. Lewis, P., et al. (2020). Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks. Advances in Neural Information Processing Systems, 33, 9459-9474. (This foundational academic paper establishes RAG as a method for models to use external knowledge, improving factual accuracy).