Demo-to-deployment gap
The distance between the conditions under which a vendor's demo was produced (curated inputs, a favorable environment, sometimes a different model version) and the conditions your production deployment will actually face. Closing this gap is the purpose of pre-signature testing on your own hardest inputs.
Defined in 2 GAGE programs, which carry 3 distinct definitions of it. The wording above is taught in Certified AI Governance Professional (CAIGP).
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.
The distance between the conditions under which a vendor's demo was produced (curated inputs, a favorable environment, sometimes a different model version) and the conditions your production deployment will actually face. Closing this gap is the purpose of pre-signature testing on your own hardest inputs.
The well-documented pattern in which a robotic system's performance in a favorable, curated demonstration does not reliably predict its performance under the full variability of a real deployment environment. Treated as its own topic later in this program. (see Topic 7.4)
The difference between a system's performance under favorable, curated demonstration conditions and its performance under the real variability, scale, and failure costs of a production deployment.
Where it is taught
The exact lessons this term appears in. The first module of every program is free with a free account.
- The vendor interrogation: questions that expose what the sales deck hides · Shipping AI and Surviving the Incident, Certified AI Governance Professional (CAIGP)
- What the judgment layer is and why it runs alongside the technical programs · Your Engineering Judgment Command Center, Engineering Judgment and Professional Formation
- Foundation models and generalist robot policies: what to try and what is still hype · Frontier and Future Horizons, Engineering Judgment and Professional Formation
Where it shows up at work
A definition is where Demo-to-deployment gap 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.
Explains training, tokens, context windows, embeddings, retrieval and fine-tuning well enough to ask an engineer a precise question and spot weak evidence.
2 lessons 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.
Understands AI that acts in the physical world: perception stacks, vision language action models, sim to real transfer, sensor fusion, and where each of them fails.
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 Security Architectneeds it at depth · 8 open postings this week
- Chief Technology Officer, AI engineering editionneeds it at depth
- AI Evaluation Specialistneeds it at depth · 6 open postings this week
- AI Governance Engineerneeds it at depth · 7 open postings this week
- AI Model Validatorneeds it at depth
- Chief AI Officerneeds 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: NIST AI RMF GOVERN 6 and MANAGE 3; ISO/IEC 42001 Annex A supplier controls.
One interview question about it
Explain how a large language model produces an answer, at the depth a governance decision needs and no deeper.
Asked of candidates for how models work, at a governance depth. 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.
- Data analysis, stewardship and business intelligence carries it as a claim to back up: SQL and analytical fluency make the technical layer approachable.
- Model risk, credit risk and quantitative analysis carries it as work you can show: Quantitative fluency is demonstrated in your work.
- A computer science or software engineering degree carries it as work you can show: You have trained or integrated a model in coursework or a project.
Terms it appears with
Not an alphabetical neighbourhood: these are the terms taught in the same lessons, ranked by how often they appear together.