Semantic search
A search method that retrieves conceptually related content even when the exact keywords do not match, as opposed to keyword search, which only finds literal word matches. Semantic search is why an AI legislative tool can find a bill like NYC's Local Law 144 even when a researcher searches for "algorithmic hiring" and the bill's actual title never uses that phrase.
Defined in 2 GAGE programs, which carry 2 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 search method that retrieves conceptually related content even when the exact keywords do not match, as opposed to keyword search, which only finds literal word matches. Semantic search is why an AI legislative tool can find a bill like NYC's Local Law 144 even when a researcher searches for "algorithmic hiring" and the bill's actual title never uses that phrase.
A search technique that retrieves results based on meaning and conceptual similarity, computed via embedding vectors, rather than exact keyword matching. The retrieval mechanism underlying RAG, agent memory, and most modern "smart search" features, and the reason personal data ends up embedded and stored in a vector database in the first place.
Where it is taught
The exact lessons this term appears in. The first module of every program is free with a free account.
- Embeddings are data too: the vector store as a personal-data system · Feeding the Machines, Certified AI Data Governance Professional (CADGP)
- Research Acceleration: AI Monitoring for Legislation, Sentiment, and Stakeholders · AI for Lobbying Productivity, The AI Lobbyist: Certified AI Policy Strategist
Where it shows up at work
A definition is where Semantic search 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.
Classifies information, applies least privilege, sets retention and acceptable-use rules, and controls what may enter a prompt, a log or an embedding.
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.
Finds every AI system in use, records owner, purpose, data and risk tier, and keeps the record alive as tools change.
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.
- Data Governance Leadneeds it at depth · 2 open postings this week
- Privacy Analystneeds working knowledge
- AI Privacy Engineerneeds it at depth · 6 open postings this week
- AI Governance Engineerneeds working knowledge · 7 open postings this week
- AI Governance Analystneeds working knowledge · 3 open postings this week
- AI Governance Coordinatorneeds 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: ISO/IEC 27001; GDPR Article 5; EU AI Act Article 4; DOL AI Literacy Framework.
One interview question about it
How do you decide who may access which data for AI work, and how long it is kept?
Asked of candidates for data classification, access and retention. 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.
- Privacy and data protection carries it as a claim to back up: Classification and retention rules are familiar territory.
- Cybersecurity and IT carries it as work you can show: Access governance and classification are implemented, not described.
- Human resources and people operations carries it as a claim to back up: Access restriction and retention for personnel data are routine.
Terms it appears with
Not an alphabetical neighbourhood: these are the terms taught in the same lessons, ranked by how often they appear together.