RAG System with Feedback: Improving Search and Answer Quality

RAG System with Feedback loops

Retrieval-Augmented Generation (RAG) systems have become a cornerstone in building advanced conversational AI and question-answering applications. By combining the strengths of retrieval systems and generative language models, RAG systems can provide accurate and contextually relevant responses. However, traditional RAG implementations may sometimes produce inconsistent or irrelevant answers due to limitations in the retrieval process or the knowledge base.

To address these challenges, we introduce a RAG system with a feedback loop. This enhanced system incorporates user feedback to dynamically adjust the retrieval process, thereby improving the quality and relevance of responses over time. In this blog post, we’ll delve into the motivation behind this approach, explore its key components and walk through a detailed implementation using Python code.

Motivation

Traditional RAG systems can sometimes falter due to:

• Inconsistent Responses: Generated answers may vary in quality.
• Irrelevant Retrievals: The retrieval component might fetch documents that are not pertinent to the user’s query.
• Static Knowledge Base: Without adaptation, the system can’t learn from past interactions.