RAGs in Enterprise

Using fine-tuned LLMs or foundational models in enterprises with strict accuracy constraints presents significant challenges. Non-RAG approaches often suffer from hallucination, confidently generating incorrect information without clear attribution, making it difficult for organizations to comply with AI regulations that require transparency and explainability. Additionally, these models tend to become stale over time, and retraining them when new data arises is challenging. Handling revisions, such as removing individuals who opt out of specific services and ensuring their data is deleted, further complicates the process. Customizing non-RAG models with domain-specific data is also problematic. However, RAG models mitigate many of these issues by grounding the LLM's output in retrievable, accurate data sources. RAGs reduce hallucination, improve factual recall, and allow enterprises to trace back to the sources or contexts used for generation, enhancing understanding...