From SharePoint to Vector DB: How Enterprise RAG Ingestion Actually Works
Series: Enterprise GenAI & RAG Architecture — Part 2 of 5
The Foundation Everything Else Depends On
In Part 1, we explained what RAG is and why it matters for enterprises. Now we’ll look at the technical foundation that makes effective retrieval possible: the ingestion pipeline.
Before a RAG system can answer a single question, it must go through a four-stage ingestion pipeline: Extract → Chunk → Embed → Store.
Each stage influences how accurately and efficiently the system retrieves relevant information. A well-designed ingestion pipeline improves retrieval quality and gives the LLM stronger context for generating useful answers. When ingestion falls short, even a highly capable model may struggle to produce consistently reliable results.
The most common reason enterprise RAG fails in production is poor chunking and ingestion, rather than the LLM itself.
Stage 1: Data Extraction — Getting Documents into the System
Enterprise data lives...
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