Hybrid RAG solutions are going to be the next big thing, and here’s why …
Organizations have been racing to implement Retrieval-Augmented Generation (RAG) to leverage unstructured data like never before. Imagine having an AI that can tap into your entire knowledge base, pulling up relevant articles, reports, or documents on demand. That’s why RAG has become one of the most commonly implemented generative AI solutions today. But what if you could take it even further?
What if your RAG solution could leverage even more data?
Hybrid RAG doesn’t just stop at unstructured data; it combines the power of both unstructured and structured data to deliver richer, more accurate results. Picture this: A customer service AI using traditional RAG can pull up a user manual (unstructured data) to assist a customer, but a hybrid RAG solution could also tap into a database containing the customer’s purchase history and past interactions (structured data) to offer tailored, actionable advice that goes beyond generic information.
Structurally, hybrid RAG works by connecting an LLM to both unstructured data sources, like PDFs in a vector database and structured data systems like SQL databases. The AI can retrieve insights from either type of data source, expanding the usability and value it can provide the user.
Given the adoption rate of RAG solutions in businesses today, it’s a safe bet that Hybrid RAG is the next popular evolution of Gen AI actually implemented in organization—giving organizations the ability to harness all their data assets for smarter decision-making and superior customer experiences.
