Repeatability
High
The same extraction logic applies to every document: pull six fields, classify sentiment, map to one of eight categories. The structure is consistent enough that a single prompt pipeline handles the full batch without per-document customization.
Ambiguity Tolerance
Medium
The output schema is crisp and the category list is controlled, but 'verbatim_quote' selection and pain_point_category assignment involve judgment calls at the margins. Success is largely verifiable by checking field completeness and category distribution, but edge cases will exist.
Data & Tool Availability
High
The files are local, finite, and small enough to process in a single session. Standard libraries handle .txt and .docx parsing, and no external APIs or live data are required beyond the LLM itself.
Error Cost
Low
The output is a CSV used for internal analysis, not a customer-facing or legally binding document. Misclassifications are easy to spot-check and correct before the data is acted on, making errors cheap to catch and fix.
Human Judgment Required
Low
Sentiment classification and category mapping from a fixed list are tasks LLMs perform well at scale. The only genuine human value-add is reviewing ambiguous edge cases and validating the top-15 pain point ranking before presenting findings.