# AI Source Integrity Field Guide

Because 'the AI said so' is not a citation.

## Product Promise
A compact reference guide for tracing, verifying, and attributing AI-assisted claims across seven source categories.

## Intended Buyer
Researchers, consultants, graduate students, analysts, and policy teams using AI in evidence-critical writing.

## Included Deliverables
- 48-page guide
- Source classification matrix
- Citation repair worksheet
- 120-entry red-flag phrase glossary
- AI-output vs. primary-source decision tree

## Practitioner Workflow
1. Source categories: define the decision, evidence, owner, and acceptance threshold before use.
2. Verification workflow: define the decision, evidence, owner, and acceptance threshold before use.
3. Citation repair: define the decision, evidence, owner, and acceptance threshold before use.
4. Red-flag language: define the decision, evidence, owner, and acceptance threshold before use.
5. Decision tree: define the decision, evidence, owner, and acceptance threshold before use.

## Operating Standard
Use this product as a practical governance aid, not as legal advice. For legal, regulatory, medical, financial, or employment decisions, require qualified human review and preserve the evidence trail.

## Evidence Rules
- Prefer primary sources over AI-generated summaries.
- Keep the raw AI output, prompts, model name, date, and reviewer identity.
- Separate verified claims from inferred, plausible, and unsupported claims.
- Do not cite AI output as a substitute for a real source.
- For volatile law, policy, science, or market claims, re-check the source close to publication.

## Official Source Anchors
- Quebec Act respecting the protection of personal information in the private sector, CQLR c P-39.1: https://www.legisquebec.gouv.qc.ca/en/document/cs/p-39.1/20240701
- NIST AI Risk Management Framework 1.0: https://www.nist.gov/itl/ai-risk-management-framework
- ISO/IEC 42001:2023 AI management systems: https://www.iso.org/standard/42001