Design of an LLM-Powered Customer Support Bot

The support bot needs two reliable paths: an evidence-backed answer and an authorized account operation. We will connect both to durable conversation state and a human handoff workflow.

High-level design of an LLM-powered customer support bot

Production support bots can become outdated quickly as product details and policies evolve, which can lead to inaccurate responses and lower user satisfaction. Systems must continuously incorporate up-to-date knowledge while managing LLM-related costs.

The following high-level design uses retrieval-augmented generation (RAG) with cost-aware routing to deliver accurate, context-rich responses. The workflow is as follows: a user submits a query through the web or mobile client. The request is processed by the API gateway for authentication, rate limiting, and session management before the gateway forwards it to the backend services. The RAG pipeline retrieves relevant knowledge from a vector database and augments the prompt, which is then passed to the LLM to generate a response. The response goes through content moderation before it is returned to the user, and the conversation is logged while feedback is collected.