Quality Assurance for AI-Generated Proposal Content Under CMMC
AI can draft proposal sections in minutes, but unreviewed AI output creates compliance and quality risks. Here's a systematic QA process for AI-generated proposal content in CMMC environments.
Cabrillo Club
Editorial Team · February 6, 2026

AI-assisted proposal writing is becoming standard practice in GovCon. Tools can draft past performance narratives, generate technical approach sections, and produce management plans in a fraction of the time manual writing requires. But speed without quality control creates two risks: submitting inaccurate content that loses evaluations, and creating compliance gaps that fail CMMC assessment.
This article extends our Compliant AI Proposal guide with a practical QA framework for AI-generated content.
The Two Risk Categories
Quality Risks
- Hallucinated facts: AI may invent contract numbers, inflate metrics, or fabricate past performance details
- Generic language: AI output often reads as plausible but generic, lacking the specificity evaluators reward
- Inconsistency across sections: Different AI-generated sections may contradict each other on team size, approach, or timeline
- RFP non-compliance: AI may miss specific RFP instructions about format, page limits, or required content
Compliance Risks
- CUI in AI training: If CUI was sent to a non-compliant AI service, the content was generated in violation of CMMC
- No audit trail: If you can't demonstrate how AI content was generated and reviewed, you have an accountability gap
- Cross-program contamination: AI accessing data from one program may leak information into content for another
The QA Process
Step 1: Pre-Generation Verification
Before generating any content, verify:
Stop losing proposals to process failures
80% of proposal time goes to tasks AI can automate. See how the Proposal Command Center accelerates every step.
See Proposal Command Center- The AI tool is approved for CUI processing (within your CMMC boundary)
- RAG sources are isolated to the relevant program (see RAG isolation requirements)
- Source documents are current and authorized for use in this proposal
Step 2: Factual Accuracy Review
Every factual claim in AI-generated content must be verified against source documents:
- Contract numbers, values, and dates match official records
- Performance metrics are accurate and sourced from CPARS or internal records
- Team member qualifications and certifications are current
- Referenced past performance is from your verified database, not AI-generated
Step 3: RFP Compliance Check
- Content addresses every evaluation criterion in the RFP
- Page limits and formatting requirements are met
- Cross-references between sections are consistent
- Required certifications and representations are accurate
Step 4: Audit Trail Documentation
For CMMC compliance, document the AI content generation process:
Stop losing proposals to process failures
80% of proposal time goes to tasks AI can automate. See how the Proposal Command Center accelerates every step.
See Proposal Command CenterCabrillo Club
Editorial Team
Cabrillo Club helps government contractors win more contracts with AI-powered proposal automation and compliance solutions.


