Repeatability
High
Every transcript goes through the same pipeline: fix OCR errors, normalize capitalization and punctuation, flag under-50-word entries. The structure is identical across all 240 rows, making this highly automatable.
Ambiguity Tolerance
High
Success criteria are explicit and measurable: corrected text, proper formatting, and a flag on short transcripts. There's no subjective quality bar beyond standard English conventions.
Data & Tool Availability
High
The data lives in a Google Sheet with a clear schema, accessible via the Sheets API or a CSV export. An LLM API (e.g., GPT-4o) can process the transcript_text column directly with no missing dependencies.
Error Cost
Low
The original transcript data can be preserved in a separate column before any writes, making every change fully reversible. Worst case is a transcript that needs a human re-edit, not a business-critical failure.
Human Judgment Required
Low
Standard OCR cleanup and punctuation normalization are well within current LLM capability. The only edge cases—ambiguous proper nouns or domain-specific jargon—are minor and can be caught in a light human spot-check.