Garbage In, Garbage Retrieved: Why Parsing Is the RAG Layer You're Underfunding
I have spent nine years building data and ML systems, and the last few of those buried in LLM and RAG pipelines on GCP and AWS. Here is the pattern I see at almost every enterprise: teams spend weeks A/B testing embedding models, tuning rerankers, and arguing about vector databases — and about 20 minutes on the code that turns a PDF into text. Then they wonder why the answers are wrong.
The uncomfortable truth: in most failing RAG systems I've audited, retrieval quality was capped long before the embedding model ever ran. It was capped at parse time. This article is a practitioner's deep-dive into the parsing/ingestion layer — the what, the why, and the how, with the tools and trade-offs I actually use in production.