The AI Engineer's Guide to Clean RAG Ingestion: How to Convert Complex PDF Whitepapers and Tables to Structured Markdown Without Hallucination Traps
Feeding raw PDF documentation into LLMs and vector databases produces catastrophic retrieval errors, broken table rows, and phantom hallucinated data. Discover how client-side structural AST parsing converts multi-column whitepapers into clean, token-efficient Markdown without transmitting proprietary IP to third-party scraping APIs.
The "Garbage In, Hallucination Out" Bottleneck in Production RAG Every machine learning engineer, LLM pipeline developer, and enterprise AI architect has hit the same brutal realization when building Retrieval-Augmented Generation (RAG) systems: your embedding models and high-end vector databases