Mei's Revenue-Cycle Prep for AI — Schema
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Flat CSV in mei-revenue-cycle.csv. One row per CFO question.

Columns
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Column          Type           Description
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id              integer        Question id (1..5) in the order Mei asked.
question        text (quoted)  The CFO-shaped question, verbatim as asked.
before_verdict  text           One of worked, ambiguous, wrong, refused.
before_notes    text (quoted)  What the before-state outcome was and why.
after_verdict   text           Same allowed set as before_verdict.
after_notes     text (quoted)  What the after-state outcome was and why.
prep_component  text (quoted)  Which Prep-for-AI component closed the gap (empty for questions that already worked before Prep).

The question, before_notes, after_notes, and prep_component fields are CSV-safe quoted strings.

Row inventory
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- 5 rows — one per CFO question.

Total: 5 rows.

Deliberate design choices
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The five-question sample size is small on purpose: Chapter 11’s point is that on a healthy hand-built model, the distribution of failures is what matters (some work, some are ambiguous, some are wrong, some are unanswerable) — not the raw count. Five questions is the smallest set that produces this full failure taxonomy.

The prep_component column names the specific fix per question so an instructor teaching the Prep-for-AI pass can walk each question’s fix in isolation. Composite values (e.g., verified_answers + ai_data_schema) capture the questions where multiple components contribute.

License
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Released for teaching use alongside The Defensible Decision. Use freely.