Enhancing Enterprise Question Answering with RAG Fusion

CIOs are constantly exploring ways to empower employees and leverage data more effectively across the organization. One area ripe for innovation is enterprise question-answering (QA) systems. Traditional QA relies heavily on outdated techniques like keyword matching, often failing to surface the most relevant information.

Retrieval-augmented generation (RAG) models have emerged as robust solutions, combining the strengths of information retrieval and natural language generation. However, even RAG models have limitations regarding comprehensively understanding and retrieving rephrased queries.

This is where a breakthrough approach called RAG Fusion comes in. By leveraging generative AI to rephrase the original query in multiple ways, RAG Fusion expands the "retrieval aperture" to encompass a broader range of relevant information across your enterprise knowledge bases.

Here's how it works:

1. A language model first generates multiple rephrasings of the user's query.
2. Each rephrased query is then embedded into a vector space along with embeddings of all your document chunks.
3. Retrieval scores are computed between the queries and documents, and a fusion algorithm intelligently combines these scores to produce a re-ranked list of relevant documents/chunks.

The benefits are clear - RAG Fusion reduces the dependency on specific query wording and surfaces a more complete set of relevant information for the user. But it gets better. Researchers have proposed optimizing RAG Fusion even further by:

• Clustering your document database to guide rephrasing toward representative topics/entities
• Constraining query rephrasing to target cluster centroids more directly
• Injecting specific domain knowledge about your products/services into the rephrasing process

Additionally, advanced RAG Fusion variants can generate potential answer sketches first to better match against document content. This makes the mapping between queries and relevant information more accurate.

As data volumes and complexity continue growing, empowering your workforce with cutting-edge QA capabilities will be crucial for maintaining competitive agility. RAG Fusion represents a powerful new paradigm that CIOs should carefully evaluate for their enterprise knowledge management strategies.

We will be watching this space closely and sharing updates on RAG Fusion and other generative AI innovations that could transform how we interact with and leverage information across the enterprise. What are your thoughts? We'd love to hear your perspectives.