This article breaks down how Databricks’ ai_parse_document and ai_query functions simplify PDF extraction in a proof-of-concept but introduce hidden challenges—ongoing costs, duplicate processing, non-deterministic outputs, and input noise—when you scale to a reliable production pipeline. It walks through the core issues and why you need additional system design for checkpointing, deduplication, deterministic validation, and PII handling before using it on real healthcare data.

• Reprocessing a 30,000-page corpus costs ~$120–$137 every time you iterate, at ~$4.20–$4.55 per 1,000 pages.
• No built-in checkpointing means any small prompt fix forces a full reparse of everything.
• Even at temperature zero, non-determinism (LLM rounding, parallel GPU execution) can flip extracted fields like follow_up_required, breaking audit trails.
• Duplicate documents (corrected/resent notes) slip through unnoticed, skewing patient cohort and diagnosis counts.