How to build an AI assistant for the enterprise

This post details the challenges and limitations of fine-tuning LLMs for enterprise data, contrasting it with Retrieval Augmented Generation (RAG). It explains why fine-tuning is not suitable for factual recall and introduces RAG as a superior approach. The post elaborates on the critical components of RAG for enterprise AI: a scalable, permission-aware indexing system (contrasting centralized indexing with federated search) and a hybrid search mechanism that combines vector search with traditional keyword retrieval and reranking. It also briefly mentions the role of an extensive knowledge graph for personalization. The technical depth lies in explaining the trade-offs of different AI approaches for enterprise data and outlining the architectural considerations for a robust RAG system.