Efficient automated customer service is a goal for many businesses, and AI-powered chatbots can provide human-like interactions without the need for live agents. This frees up the agents for more complex tasks. Among these AI-powered tools are Amazon Lex and Amazon Bedrock. Lex offers advanced conversational interfaces, utilizing voice and text channels to provide accurate understanding of user intent.
Bedrock simplifies the development and scaling of applications powered by large language models (LLMs) and foundation models (FMs). This resource offers access to FM’s from providers including Anthropic Claude, AI21 Labs, Cohere, Stability AI, and Amazon’s Titan models. Their Knowledge Bases for Amazon Bedrock allows for the development of applications that use Retrieval Augmented Generation (RAG), where information from data sources can inform the model’s response.
Also explored is the generative AI capability of QnAIntent, which securely links FMs to company data for RAG. This tool can help deploy the right information to customer inquiries, improving chatbot interactions.
The post gives a detailed demonstration of how to build chatbots with QnAIntent and Amazon Bedrock. The process includes creating a knowledge base for OpenSearch Serverless, creating an Amazon Lex bot, creating new generative AI-powered intent using QnAIntent, and deploying the web UI. The solution and its workflow are illustrated with a diagram.
The authors also provide a pre-requisite checklist outlining what is required before implementing the solution, comprising the needed AWS resources, understanding of relevant AWS services, and a data source in Amazon S3. Further detailed steps guide the reader through creating a knowledge base, setting up an Amazon Lex bot, adding QnAIntent to the bot’s intent, and deploying the Amazon Lex web UI. Instructions for cleaning up to avoid unnecessary charges are also provided.
In conclusion, the significance of generative AI-powered chatbots is underscored, particularly in customer support systems. The practical use case described in the post, which analyzes Amazon shareholder documents, demonstrates the functionality and benefits of this setup. The implementation can provide prompt and consistent customer service while ensuring live agents can focus on resolving more complex issues. The authors encourage continued exploration of generative AI and keep up to date with latest advancements in this field.
