What is this course about?
Generic AI solicitations attract proposals that cannot be compared. This course builds requirements that separate capability from marketing, mandatory disclosures on models and data handling, scoreable evaluation criteria, and the data rights and exit provisions that determine whether the agency stays in control after award.
Who is this course for?
- Contracting officers and specialists
- Program managers writing requirements
- Technical evaluation panel members
- Agency counsel supporting acquisition
What do I need before starting?
- Familiarity with your agency's acquisition process
- No technical background required
What will I be able to do afterwards?
- Write requirements that separate capability from vendor marketing
- Mandate the disclosures needed to evaluate an AI system
- Build evaluation criteria that are genuinely scoreable
- Compare per-seat against usage and owned pricing in a solicitation
- Secure data rights, code escrow, and exit provisions
What does each module cover?
Why do generic AI solicitations fail?
40 minHow vague requirements produce incomparable proposals and a protest-prone evaluation.
Objectives
- Diagnose why a solicitation produced unusable proposals
- Identify requirement language that invites marketing
- Set specificity standards
Topics
Activity. Critique a real AI solicitation for language that produced marketing responses.
How do you specify capability rather than features?
50 minPerformance-based requirements that a vendor must demonstrate rather than assert.
Objectives
- Write performance-based capability requirements
- Specify demonstrations rather than claims
- Avoid requirements that lock in one architecture
Topics
Activity. Rewrite five feature requirements as demonstrable capability requirements.
What must a vendor disclose?
50 minModels, training data, subprocessors, and residency — the disclosures without which evaluation is guesswork.
Objectives
- Mandate the disclosures needed for evaluation
- Require them in a comparable format
- Handle claimed proprietary exemptions
Topics
Activity. Draft the mandatory disclosure section with a required response format.
How do you build criteria that are actually scoreable?
50 minEvaluation criteria a panel can apply consistently and defend in a protest.
Objectives
- Write criteria that produce consistent scores
- Weight criteria before receiving proposals
- Document the evaluation defensibly
Topics
Activity. Build the evaluation rubric and test panel consistency on a sample proposal.
How do you compare pricing shapes fairly?
45 minStructuring the price schedule so per-seat, usage, and owned proposals can be compared at all.
Objectives
- Structure a price schedule that normalizes across shapes
- Require lifecycle rather than year-one pricing
- Model cost at realistic usage
Topics
Activity. Design a price schedule that makes three pricing shapes comparable.
What data rights must the agency keep?
45 minData rights, code escrow, and the terms that determine post-award control.
Objectives
- Specify required data and software rights
- Determine when escrow is warranted
- Protect agency data from vendor model training
Topics
Activity. Draft the data rights and training prohibition clauses.
How do you avoid a captive renewal?
45 minPilot-to-production terms that keep a real competitive option at the next decision point.
Objectives
- Structure pilot terms that preserve competition
- Require exit assistance and data return
- Estimate switching cost before award
Topics
Activity. Draft the exit provisions and estimate switching cost for a proposed award.
Building the solicitation section
55 minThe workshop module: a complete AI solicitation section with an evaluation rubric.
Objectives
- Assemble the complete solicitation section
- Attach the evaluation rubric
- Review with contracting and counsel
Topics
Activity. Assemble the section and have a contracting officer review it.
What is the capstone project?
AI solicitation section with an evaluation rubric
Produce a complete solicitation section: performance-based capability requirements, mandatory vendor disclosures with a response format, a scoreable weighted evaluation rubric, a normalized price schedule, data rights and training prohibitions, and exit provisions.
Deliverable: A solicitation section reviewed by a contracting officer and agency counsel.
How are learners assessed?
- Panel consistency test — two evaluators must score a sample proposal similarly
- Requirements checked for architecture neutrality
- Exit provisions reviewed for whether they produce a real option
What ships with the course?
Facilitator guide
Session-by-session running order, discussion prompts, and the questions that reliably derail a room.
Learner workbook
Exercises, checklists, and the templates each module's activity produces.
Hands-on lab environment
A sandboxed ibl.ai deployment so exercises run against real agents, not screenshots.
Assessment bank
Scenario questions and rubric criteria mapped to each stated learning outcome.
Source bibliography
Every primary regulation and standard cited on this page, linked and dated.
Which AI agents does this course use?
The hands-on modules run against agents already deployable on the ibl.ai platform for government.
Where does the course material come from?
Every module is grounded in primary sources — the regulation, standard, or research itself, not a summary of it. Each was resolved at authoring time.
- Federal Acquisition Regulation
Acquisition.gov
The regulatory framework governing the solicitation language.
- AI Guide for Government
GSA Centers of Excellence
Federal acquisition practice guidance for AI.
- FedRAMP
FedRAMP PMO
Authorization requirements that must be reflected in the solicitation.
- AI Risk Management Framework
NIST
Risk requirements referenced in the mandatory disclosures.
Delivery notes
Binding guidance for anyone preparing and delivering this course.
- Module 4's panel consistency test is the protest-prevention exercise. Criteria that two evaluators score differently are criteria that will be protested.
- Every clause must be reviewed by a contracting officer before delivery. Solicitation language written by technologists creates acquisition problems even when the technical content is right.
- Module 2 must stay architecture-neutral. Requirements that only one vendor's design can satisfy invite protest regardless of whether that design is best.
- State and local procurement differ substantially from federal. Localize the module for non-federal cohorts rather than assuming the FAR.
- Do not write clauses that only ibl.ai can meet. The course's credibility with contracting officers depends on the requirements being genuinely neutral, and neutral requirements already favor an owned deployment on the merits.
Why run AI training on a platform you own?
You own the course, not a licence to it
Course content, learner data, and the platform run inside your perimeter — you own all the code and the data.
Model-agnostic delivery
Run the course's AI components on any LLM — Claude, GPT, Llama, Gemini, Command — and switch anytime.
No per-seat training licences
Usage-based or self-hosted, so cost tracks actual use rather than headcount.
Deploy anywhere
Cloud, private VPC, on-premise, or fully air-gapped — including for cohorts that cannot use public AI tools.
Frequently asked questions
What does the AI in Public Procurement: Writing an RFP That Gets Real Bids course cover?
Generic AI solicitations attract proposals that cannot be compared. This course builds requirements that separate capability from marketing, mandatory disclosures on models and data handling, scoreable evaluation criteria, and the data rights and exit provisions that determine whether the agency stays in control after award. It runs 5.5 hours across 8 modules across 8 modules, at intermediate level, and closes with a capstone: AI solicitation section with an evaluation rubric.
Who should take AI in Public Procurement: Writing an RFP That Gets Real Bids?
It is written for Contracting officers and specialists, Program managers writing requirements, Technical evaluation panel members, Agency counsel supporting acquisition. Prerequisites: Familiarity with your agency's acquisition process; No technical background required.
Can we run this course on our own infrastructure?
Yes. ibl.ai is model-agnostic and deploy-anywhere — cloud, private VPC, on-premise, or fully air-gapped — and you own all the code and the data. Cohort data, submissions, and any material learners upload stay inside your perimeter, which matters for government teams that cannot send work to a public AI tool.
How do we get access to AI in Public Procurement: Writing an RFP That Gets Real Bids?
Request access and we will set it up for your cohort — hosted by ibl.ai, or running against your own deployment. Tell us the group size and timing you need, and whether it should run inside your own perimeter.
How much does AI training for government cost on ibl.ai?
There is no per-seat pricing — you pay for usage or self-host and pay only for the infrastructure, so a 5,000-person rollout does not cost 5,000 licences. 1.6M+ users across 400+ organizations run the platform this way, including NVIDIA, MIT, and Syracuse University.