Contextual Retrieval In Anthropic Using Amazon Bedrock Knowledge Bases
AWS, Thursday, June 5th, 2025
For an AI model to perform effectively in specialized domains, it requires access to relevant background knowledge. A customer support chat assistant, for instance, needs detailed information about the business it serves, and a legal analysis tool must draw upon a comprehensive database of past cases.
To equip large language models (LLMs) with this knowledge, developers often use Retrieval Augmented Generation (RAG). This technique retrieves pertinent information from a knowledge base and incorporates it into the user's prompt, significantly improving the model's responses. However, a key limitation of traditional RAG systems is that they often lose contextual nuances when encoding data, leading to irrelevant or incomplete retrievals from the knowledge base.