Most Enterprises Build Fragile RAG Pipelines - Here is How to Architect Compound AI Systems

Most enterprises building AI applications on their data start with a naive Retrieval-Augmented Generation (RAG) pipeline: chunking documents, embedding them into a vector database, and doing a semantic search. But when they try to deploy this to production for enterprise Business Intelligence (BI), it quickly becomes fragile and breaks down. The core issue is that standalone LLMs and naive vector search were never designed to solve enterprise BI. Vector search is excellent for unstructured simi...