The long-standing effort to manage enterprise knowledge has repeatedly failed due to the inability of software to process noisy, unstructured data. That is until now.
LLMs and GenAI offer many powerful tools and technologies for increased innovations and efficiency in dealing with unstructured data.
Among the leading approaches in this space, the combination of Retrieval-Augmented Generation (RAG) and AI agents has quickly emerged as the dominant approach for deploying generative AI in the enterprise.
However, as with any cutting-edge technology, successfully implementing these solutions comes with its own set of challenges.
Chandini Jain, founder and CEO of Auquan, has discussed RAG and AI agents in detail including use cases, challenges, and their solutions in her latest InfoWorld article. Some of the key questions answered include:
“...RAG introduces an information retrieval component to generative AI, allowing systems to access external data beyond an LLM’s training set and constrain outputs to this specific information. And by deploying a sequence of AI agents to perform specific tasks, teams can now automate entire complex, multi-stage knowledge workflows using RAG—tasks that previously could only be performed by humans.”
“...the real magic of RAG is in the retrieval model and its upstream components. RAG deployments live and die by the quality of the source content and the retrieval model’s ability to filter the large data source down to useful data points before feeding it to an LLM…”
“...a well-designed AI agents approach to the automation of complex knowledge workflows can help mitigate risks with RAG deployments by breaking down large use cases into discrete ‘jobs to be done…’”
Whether you work in finance, technology, or any data-driven industry, this article is essential reading for understanding the future of AI in the enterprise.