# The “CSV Basics” Checklist for Your First Project

> New teams often start with documents instead of decisions. A simple checklist keeps your first project practical and

- Author: Clara Bennett (https://lifescienceai.org/authors/clara-bennett/)
- Published: 2026-06-02
- Category: Fundamentals
- Canonical URL: https://lifescienceai.org/articles/csv-basics-checklist-for-your-first-project/
- Word count: 192
- Platforms named: Qualitum (https://qualitum.ai/)

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## First CSV projects fail for predictable reasons

New teams often start with documents instead of decisions. A simple checklist keeps your first project practical and audit-friendly.

## The checklist

1) Intended use written clearly.

2) Scope boundary defined.

3) Critical functions and critical data identified.

4) Risks mapped to controls.

5) Requirements are testable.

6) Tests prove controls, not screens.

7) Evidence is stored with an index.

8) Operational controls planned. Access review, audit trail review, change control triggers.

CSV becomes manageable when you treat it as structured reasoning with evidence, not as a document collection exercise.

## How this looks with an agentic layer

The fundamentals above do not change when validation is automated - they get enforced earlier. On a layer like Qualitum, intended use, risk and evidence are structured inputs rather than documents someone remembers to write: agents draft against them, maintain the traceability, and hand the result to a named human for approval.

- Intended use and critical data are captured once and reused across the lifecycle
- Evidence is generated with its traceability attached, not reconstructed for the audit
- The approval - and the accountability - stays with your named reviewer

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Source: LifeScienceAI - https://lifescienceai.org/articles/csv-basics-checklist-for-your-first-project/. Editorial analysis, not regulatory advice. Cite as: Clara Bennett, "The “CSV Basics” Checklist for Your First Project", LifeScienceAI, June 2, 2026.