AI-assisted Validation-document Review Methodology

Problem addressed

High-volume document review is slow and inconsistent, yet unconstrained AI can omit, hallucinate, or misclassify evidence.

Limitation of existing practice

Generic summarization treats fluent output as correctness and obscures source provenance.

Proposed method: inputs and steps

1. Define permitted review tasks and prohibited decisions.
2. Parse documents without changing source records.
3. Retrieve traceable source passages.
4. Apply deterministic checks before probabilistic review.
5. Require human confirmation for findings.
6. Measure misses, false findings, override, and drift.

Decision outputs

• Traceable review findings
• Human disposition record
• Performance and control report

Worked example

CSVDoc Reviewer flags missing approvals, inconsistent identifiers, and requirement-test gaps; every finding links to source text and requires reviewer disposition before export.

When to use—and when not to

Use when: Prioritizing controlled-document review with accessible source evidence and competent reviewers.

Do not use as-is when: For autonomous approval, final compliance decisions, or documents that cannot be processed under privacy and confidentiality controls.

Limitations

Performance depends on document quality, parsing, retrieval, model behavior, and reviewer vigilance.

Inspection questions

• Which decisions remain human?
• How are false negatives estimated?
• Can each finding be traced to immutable source evidence?