Why AI-Drafted Documents Need a Distinct Review Step
AI tools are genuinely useful for drafting the first version of an RFI, submittal cover letter, report section, or proposal — they're fast, and they produce fluent, well-organized text. The risk isn't that AI drafting is inherently unreliable; it's that a fluent, professionally formatted draft is easy to skim and approve without the same scrutiny a rougher human first draft would naturally invite. A deliberate review workflow counteracts that — it treats AI-assisted drafts as requiring the same or greater scrutiny as a human first draft, not less.
A Four-Stage Review Workflow
- Fact and citation check. Verify every factual claim, code reference, specification citation, or technical detail in the AI-drafted document against its actual source. This is the single highest-value review step, since fabricated or mismatched citations are the most common and most damaging AI failure mode in written technical documents.
- Technical accuracy review by a qualified reviewer. Someone with the technical background to evaluate the document's substance — not just its prose — reviews the content for correctness, completeness, and whether it actually says what the project needs it to say. For RFIs and submittals specifically, confirm the AI draft correctly represents the actual field condition, spec section, or design intent, not a generic or templated version of it.
- Tone and context review. AI-drafted client-facing documents can read as generic or miss project-specific nuance a human author would naturally include — a reviewer familiar with the client relationship and project history checks that the document reads as genuinely responsive to this specific situation, not a template with details swapped in.
- Sign-off and documentation. The final reviewer signs off explicitly — initials and date on the document or in the project record — confirming review occurred. This step matters less for the review itself and more for creating a record that review happened, which matters if the document is ever questioned later.
Document-Type-Specific Considerations
RFIs: confirm the AI draft accurately represents the actual field or design condition prompting the question — an AI tool asked to "write an RFI about a duct clearance conflict" will produce a plausible-sounding RFI regardless of whether it correctly captures the actual conflict, so the underlying technical description needs direct verification against the real condition.
Submittals: verify that AI-drafted submittal cover letters or transmittal language correctly reference the actual specification section, product data, and any substitution requests — a generic-sounding submittal letter that doesn't precisely match the spec section it's responding to is a common plan-reviewer red flag.
Reports: check that AI-assisted report sections accurately reflect the actual project data, calculations, or field observations they describe, not a plausible-sounding generic version of what such a report typically contains.
Proposals: confirm AI-drafted scope, fee, or schedule language matches what was actually discussed with the client and is internally consistent with other proposal sections — AI tools can produce proposal language that sounds right but doesn't match specific commitments already made verbally or in earlier correspondence.
Making the Review Efficient, Not Just Thorough
A review workflow that's too heavy to actually follow gets skipped under deadline pressure. Keep the process proportional to the document's stakes — a routine internal status report warrants a lighter check than a submittal package headed to a general contractor and eventually an owner. Building a short, reusable checklist per document type (see the companion AI Output Verification Checklist) makes the review fast enough to actually happen consistently rather than being the first step cut when a deadline tightens.