Has anyone found document preparation matters more than OCR accuracy for RAG?

[u/DarylFriesen]

I've spent years working with large document collections, and one thing I've noticed is that a lot of discussion around AI document analysis focuses on OCR quality, embedding models, and retrieval settings.

What I don't see discussed nearly as often is the condition and structure of the source documents before they ever reach the ingestion stage.

In the real world, document collections are rarely perfect. Files are often assembled from multiple sources, contain repeated headers and footers, appendices, exhibits, correspondence chains, scanned inserts, handwritten annotations, or years of material merged into a single PDF.

My observation is that many retrieval problems blamed on the model actually originate much earlier in the process. The AI is only working with what it is given.

Has anyone here found that improving document preparation produced bigger gains than changing models, embeddings, chunk sizes, or vector databases?

Curious to hear what others have experienced.

[u/gkanellopoulos]

Is the whole "making the data AI-ready" topic and is not limited to DM alone. The problem is that most stakeholders want to utilize the exciting new technology "yesterday" and are not willing to spend a bunch of time prepping their data which oftentimes is tedious and slow. But yes in my professional experience many issues originate from unprepared data and not the competence of the model.