RAG knowledge bases add more data preparation work
As companies put into real business use, they become more willing to connect internal information to those assistants. That information can include documents, , manuals, product specs, policies, reports, call transcripts, and expert knowledge spread across separate systems. This creates more demand for RAG .
The main work happens before indexing, when the data has to be prepared. Company data is often messy: duplicate files, old versions, long PDFs, uneven formatting, tables, screenshots, multiple languages, missing metadata, and text that was not written for machine search. A practical RAG workflow needs cleaning, chunking, filtering, metadata extraction, , evaluation, and ongoing updates.
A is only useful when the behind it is reliable. OpenDCAI/DataFlow aims to make data preparation for RAG and apps easier to repeat, inspect, and automate.
Key points
- Business use of is increasing demand for RAG connected to internal data.
- The biggest workload is preparing data before indexing it.
- Common problems include duplicate files, old versions, long PDFs, tables, screenshots, mixed languages, and missing metadata.
- A practical workflow includes cleaning, chunking, filtering, metadata extraction, , evaluation, and ongoing updates.
- Cleaner data can help AI agents retrieve better context and avoid wasting tokens.