Repeatability
High
The same five output columns are extracted from every document, and the target sections (project description, budget narrative, org background) are standard grant writing conventions. Structural variation across funders exists but is predictable enough for a robust extraction prompt.
Ambiguity Tolerance
Medium
The output schema is well-defined, but 'project_description' and 'org_background' can span many pages and require judgment about where a section starts and ends, especially when PDFs lack clear headers. Budget totals are usually findable but may require parsing tables or narrative text.
Data & Tool Availability
High
The user holds all 180 PDFs and the output format is a simple CSV. OCR tools (e.g., AWS Textract, Adobe, or open-source alternatives) and LLM-based extraction pipelines are mature and accessible. No external APIs or permissions are needed beyond file access.
Error Cost
Low
Errors produce a searchable archive with some inaccurate or missing fields — annoying but not damaging. The source PDFs remain intact, so any extraction mistake is fully reversible by re-running or manually correcting individual rows.
Human Judgment Required
Low
No subjective taste or relationship context is needed — this is document parsing and data entry. A human spot-check of 10–15 rows is advisable to validate extraction quality, but the core work does not require human judgment.