Sycophancy
A large language model's tendency to produce agreeable, user-pleasing output by default, which in a persona simulation manifests as softened stakeholder objections unless explicitly countered with an instruction to argue forcefully.
Defined in 2 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 language model's tendency to produce agreeable, user-pleasing output by default, which in a persona simulation manifests as softened stakeholder objections unless explicitly countered with an instruction to argue forcefully.
The tendency of models trained on human feedback to tilt toward the user's stated or implied belief, producing faithfulness breaks toward the framing rather than reality (Anthropic, Persona Vectors, 2025; Wang et al., 2025).
A tendency in conversational AI tools, tuned to be agreeable to the user, to let the user's own side of a debate win too easily unless explicitly instructed to argue the opposing side at genuine strength.
Where it is taught
The exact lessons this term appears in. The first module of every program is free with a free account.
- Spotting AI Biases and Hallucinations: Structural vs. Factual Errors · Critical Thinking and Context Engineering, Certified AI Practitioner: Workplace Foundations
- Stakeholder Persona Simulation: Modeling Positions, Concerns, and Objections · Prompt Engineering for Strategic Persuasion, The AI Lobbyist: Certified AI Policy Strategist
- The Prompt Library and the Live AI Debate Simulation · Prompt Engineering for Strategic Persuasion, The AI Lobbyist: Certified AI Policy Strategist
Where it shows up at work
A definition is where Sycophancy 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.
Recognizes hallucination, drift, skew, brittleness and biased outcomes, and knows how each one enters a system.
One lesson on this page teach it as a primary outcome.
Briefs a model like a good manager: context, constraints, examples, verification, and knows when to stop delegating.
One lesson on this page teach it as a primary outcome.
Uses generative AI tools daily, knows what a model can and cannot do, and can say where an output should not be trusted.
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 Literacy Leadneeds it at depth
- Model Risk Managerneeds it at depth
- AI Evaluation Specialistneeds it at depth · 6 open postings this week
- AI Model Validatorneeds it at depth
- Ethical AI Specialistneeds it at depth · 5 open postings this week
- Operations Manager, AI-readyneeds it at depth
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 MEASURE 2; ISO/IEC 42001; EU AI Act Article 4; DOL AI Literacy Framework.
One interview question about it
Tell me about a time a model was confidently wrong. How did you notice, and what did you change afterwards?
Asked of candidates for model failure modes and bias recognition. 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.
- A data science, statistics or analytics degree carries it as work you can show: Bias and failure modes were in your projects, not only your reading.
- A computer science or software engineering degree carries it as work you can show: Prompting and tool workflows are already in your daily work.
- Recent graduate in law, policy, business or data carries it as a claim to back up: Most graduates already use the tools daily.
Terms it appears with
Not an alphabetical neighbourhood: these are the terms taught in the same lessons, ranked by how often they appear together.