Generative AI (GenAI) and large language models (LLMs), such as those available soon via Amazon Bedrock and Amazon Titan are transforming the way developers and enterprises are able to solve traditionally complex challenges related to natural language processing and understanding. Some of the benefits offered by LLMs include the ability to create more capable and compelling conversational AI experiences for customer service applications, and improving employee productivity through more intuitive and accurate responses.
For these use cases, however, it’s critical for the GenAI applications implementing the conversational experiences to meet two key criteria: limit the responses to company data, thereby mitigating model hallucinations (incorrect statements), and filter responses according to the end-user content access permissions.
To restrict the GenAI application responses to company data only, we need to use a technique called Retrieval Augmented Generation (RAG). An application using the RAG approach retrieves information most relevant to the user’s request

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