Welcome to HCLS AI/ML Content Catalog
Regulatory scientists are highly educated experts responsible for managing thousands of interactions with authorities during drug development.
The tools they use to access and search data are inefficient, requiring extensive institutional knowledge. This makes it hard to find needed information and slows the process of bringing new medicines to patients.
In this demo, we use Amazon Kendra and Amazon Bedrock to build a “Search+RAG” workflow for FDA drug approval documents.
Retrieval augmented generation, or “RAG”, is a generative AI pattern where you include relevant background information when asking a question to a LLM. Search+RAG expands upon this by also returning relevant documents to the user in addition to the LLM response. This improves traceability and confidence in the LLM-generated answer.
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