Summary: Amazon Pharmacy has developed a generative AI question and answering (Q&A) chatbot assistant to help customer care agents retrieve information in real time. The solution uses the Retrieval Augmented Generation (RAG) pattern and is HIPAA compliant. Agents provide feedback on the machine-generated answers, which is used for future model improvements. The chatbot is integrated into the customer care application and accelerates ML model development using Amazon SageMaker JumpStart. The solution improves the customer care experience for pharmacy patients.
Introducing Amazon Pharmacy’s AI-Powered Customer Care Assistant
Amazon Pharmacy has implemented a powerful AI chatbot assistant to enhance the customer care experience for pharmacy patients. This AI assistant is designed to help customer care agents quickly retrieve information and provide accurate answers to customer questions in real time. By using natural language searches, agents can easily find the precise information they need, improving the speed and efficiency of customer service.
Key Features and Benefits:
– Transparent pricing, clinical and customer support, and free delivery
– Chat interface for online communication with customer care agents
– HIPAA compliant to ensure customer privacy
– Agents provide feedback to improve the AI model
– Accelerated ML model development using SageMaker JumpStart
– Retrieval Augmented Generation (RAG) design pattern for Q&A solutions
– Knowledge base management using Amazon S3
– Modular microservices architecture for scalability and flexibility
– Multi-tenant solution supporting additional health products
How the AI-Powered Chatbot Works
The customer care agent interacts with the AI-powered chatbot through a separate internal UI. The agent asks questions to the chatbot, which uses large language models (LLM) to generate accurate responses. The chatbot’s responses are reviewed by the agent before being provided to the customer.
Accelerating ML Model Development
The development team at Amazon Pharmacy used SageMaker JumpStart to quickly experiment with different models and benchmarks. This allowed them to choose the most effective model and customize it as needed. By leveraging foundation models in SageMaker JumpStart, the team saved months of work that would have been required to train models from scratch.
The RAG Design Pattern
The chatbot solution uses the Retrieval Augmented Generation (RAG) design pattern. This involves creating a knowledge base of question and answer pairs, converting questions into embeddings for efficient searching, and using similarity models to retrieve relevant answers. The generative step involves using the large language models to generate the final response.
Managing the Knowledge Base
To facilitate indexing and retrieval, the chatbot’s knowledge base is consolidated into a single repository using Amazon Simple Storage Service (Amazon S3). This allows for efficient searching and retrieval of information.
Solution Overview
The solution architecture includes separate VPCs for the customer care application and the chatbot, ensuring network isolation. AWS PrivateLink is used to securely connect the components. The chatbot logic is hosted on AWS Fargate with Amazon Elastic Container Service (Amazon ECS), and the primary storage service is Amazon S3. SageMaker is used for hosting the ML models, and feedback from agents is stored in a separate S3 bucket.
Conclusion
Amazon Pharmacy’s AI-powered customer care assistant improves the customer care experience by providing quick and accurate answers to customer questions. The solution leverages AI technologies and follows responsible AI principles to ensure privacy and security. With its modular architecture and use of foundation models, the solution is scalable and customizable for different health products.
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List of Useful Links:
- AI Lab in Telegram @aiscrumbot – free consultation
- Learn how Amazon Pharmacy created their LLM-based chat-bot using Amazon SageMaker
- AWS Machine Learning Blog
- Twitter – @itinaicom
