Foundation model
A large AI model trained on broad, general-purpose data that can be adapted to a wide range of downstream tasks, forming the technical "foundation" underneath many specific generative AI products. The term is used across policy documents, including in the EU AI Act's general-purpose AI provisions, to distinguish these broad models from narrowly purpose-built systems.
Defined in 3 GAGE programs, which carry 3 distinct definitions of it. The wording above is taught in The AI Lobbyist: Certified AI Policy Strategist.
How each discipline defines it
The same term does different work depending on who is using it. These are the definitions as each program teaches them, unedited.
A large AI model trained on broad, general-purpose data that can be adapted to a wide range of downstream tasks, forming the technical "foundation" underneath many specific generative AI products. The term is used across policy documents, including in the EU AI Act's general-purpose AI provisions, to distinguish these broad models from narrowly purpose-built systems.
A large AI model trained on massive data that can be adapted to many downstream tasks. Foundation-model providers concentrate ecosystem value and increasingly expand into the application layer their own customers occupy.
A common industry term for a large, general-purpose model trained on broad data and adapted to many uses. Overlaps in practice with the EU AI Act's "GPAI model," which is the term the law actually uses.
Where it is taught
The exact lessons this term appears in. The first module of every program is free with a free account.
- GPAI upstream: what your foundation-model vendor owes you and what you must verify yourself · The EU AI Act: The Executive Map, Certified AI Governance Professional (CAIGP)
- The Only Ten Things About AI That Matter in Policy · The Point Person Mandate, The AI Lobbyist: Certified AI Policy Strategist
- Ecosystem and Open-Source Strategy · Technology, Platforms and Vendors, Certified AI Transformation Professional (CATP)
Where it shows up at work
A definition is where Foundation model starts. The lessons above teach it inside the competencies below, which are what job descriptions ask for and what the GAGE credential records. Every link here is drawn from the same careers map that builds a role's readiness check.
Turns controls into enforceable obligations: data use, change notice, audit rights, incident duties, subcontractors, IP, exit and deletion.
One lesson on this page teach it as a primary outcome.
Tiers vendors by use and impact, requests evidence instead of promises, tests in the customer's context, and plans monitoring and exit.
One lesson on this page teach it as a primary outcome.
Writes requirements, runs a fair evaluation, pilots within limits, and refuses a demo as evidence.
One lesson on this page teach it as a primary outcome.
Roles that hire for it
Each role page shows the full competency set, what the seat decides, and a readiness check against your own evidence.
- AI Vendor Risk Managerneeds it at depth
- Third-Party AI Risk Analystneeds it at depth
- Third-Party Cyber Risk Managerneeds it at depth
- AI Regulatory Counselneeds it at depth · 14 open postings this week
- Chief Information Officer, technology leadership editionneeds it at depth
- Chief Audit Executive, AI audit editionneeds working knowledge
How job descriptions say it
Postings rarely use the textbook word. These are the phrases the careers map reads as asking for the competencies above.
Where the frameworks place it: GDPR Article 28; EU AI Act value-chain obligations; NIST AI RMF GOVERN 6 and MANAGE 3; ISO/IEC 42001 Annex A supplier controls.
One interview question about it
Which contract terms would you insist on before a vendor's model touches customer data, and what happens when the vendor changes the model?
Asked of candidates for contract terms that allocate ai risk. Answering it in your own words is exactly how a GAGE topic is passed, and the credential records the pass where an employer can check it.
If you come from another field
Some backgrounds already carry this competency, and the readiness check gives credit for it.
- Legal and paralegal work carries it as work you can show: Contract terms are your product.
- A law degree (JD, LLB or LLM), no AI work yet carries it as a claim to back up: Contract terms that move risk are a first-year subject.
- Privacy and data protection carries it as a claim to back up: Processor and vendor review is routine privacy work.
Terms it appears with
Not an alphabetical neighbourhood: these are the terms taught in the same lessons, ranked by how often they appear together.