RAG with Conversation History Powered GenAI

Brillio’s RAG conversation solution refers to an advanced approach in retrieval-augmented generation (RAG) that utilizes historical dialogue context alongside a structured knowledge base to enhance the quality and relevance of generated responses. By leveraging past conversations, this method improves the relevance and coherence of generated outputs, enabling more personalized and context-aware interactions. This integration helps systems better understand user intent and provide accurate, contextually appropriate responses, making it particularly useful in applications like customer support and virtual assistants. The solution primarily utilizes Amazon S3, Lambda, AWS OpenSearch, AWS Bedrock services, DynamoDB for RAG conversation.

• Raw documents are extracted, processed and transformed to the vector form using an embeddings model.

• For fulfilling user request using the Knowledge Base of SharePoint, user asked query is routed to extract relevant context from the Vector Db.

• Relevant context for the user asked query is extracted from the Vector Db in chunks and sent back to the application and logical component.

• Application and logical component send the relevant context along with the prompt to the LLM component, to get the relevant response.

• By utilizing conversation history, RAG can generate responses that are more relevant and context-aware, leading to a more natural interaction flow.

• Maintaining context throughout the conversation increases user engagement and satisfaction, as users feel understood and valued.

• RAG can tailor responses based on past interactions, creating a more personalized experience that caters to individual user preferences and needs.

• The ability to pull in relevant external data enhances the quality and accuracy of responses, providing users with the most up-to-date information.

• Quick access to relevant information and previous interactions can lead to faster response times, enhancing overall efficiency in user interactions.

• RAG systems can handle a high volume of concurrent conversations while maintaining context, making them ideal for customer service and support applications.

• Customer Support: In customer service environments, this method enables agents or chatbots to recall previous support interactions, significantly improving response times and the relevance of solutions provided.

• E-commerce: Online shopping platforms can use this approach to remember user preferences, past purchases, and inquiries, enhancing product recommendations and customer engagement.

• Healthcare: Telehealth systems can track patient histories and previous consultations, allowing healthcare providers to offer more informed advice and follow-ups.

• Education: Learning platforms can personalize the educational experience by recalling student interactions and performance, thereby tailoring content and support to individual learning paths.

• Social Media Management: Bots can maintain context in ongoing conversations with users, enhancing engagement by responding appropriately based on previous interactions.