Multi-Agent Generative AI System for Reducing Hallucinations
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Solution Overview
Problem
Existing generative AI chatbots often experience 'hallucination' when handling complex customer support issues, leading to contextually plausible but factually incorrect or fabricated responses.
Innovation Solution
A multi-agent generative AI system comprising a chat agent, a triage agent, and a retrieval agent, which processes user inputs to generate accurate responses by retrieving relevant documents from a domain-specific vector database.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a single generative AI chatbot handles complex customer support issues, then the system is simple and easy to operate, but the chatbot produces hallucinations (factually incorrect responses)
Solution Approach 1:
The system divides the chatbot functionality into separate specialized agents: a retrieval agent for fetching relevant documents, a triage agent for determining information sufficiency, and a chat agent for generating responses. This segmentation allows each agent to focus on specific tasks, improving overall reliability while managing complexity through modular design.
Solution Approach 2:
The triage agent acts as an intermediary between the retrieval agent and the chat agent. It receives retrieved documents, evaluates whether they contain sufficient information, and decides whether to proceed with response generation or request additional retrieval. This intermediary layer prevents hallucinations by filtering out cases where information is insufficient.
2Reliability
If the system retrieves and verifies documents from a vector database, then response accuracy improves, but computational resources and data storage requirements increase
Solution Approach 1:
The system performs preliminary retrieval of relevant documents before generating responses. The retrieval agent proactively fetches documents from the vector database based on the user query, and the triage agent evaluates these pre-retrieved documents to determine if they contain sufficient information. This preliminary action ensures accurate responses while optimizing resource usage by only processing necessary documents.
Solution Approach 2:
The triage agent autonomously evaluates the retrieved documents and determines whether additional retrieval is needed or if the chat agent can proceed with generating a response. This self-service mechanism optimizes computational resources by avoiding unnecessary retrieval operations and document processing, while still ensuring response accuracy through selective verification.
Data Source
AI summary
A multi-agent generative AI system is described. The system includes at least one data storage device storing a vector database, in which the vector database includes (i) a plurality of documents, and (ii) for each document of a plurality of documents, a respective document embedding that represents a semantic meaning of the document; at least one processor; and a memory communicatively coupled to the at least one processor, the memory storing instructions which, when executed by the at least one processor, cause the at least one processor to implement a computerized chat agent, a computerized retrieval agent, and a computerized triage agent.