RAG (Retrieval-Augmented Generation)
An AI system architecture in which a model retrieves relevant documents or data from an external store at query time and includes them in its prompt, rather than having been trained directly on that data. Removing a document from the retrieval store generally prevents its future retrieval, a retention advantage over fine-tuning, though the retrieval store's embeddings, any cached prior responses that already included the document's content, backup copies of the index, and vendor training-on-query terms remain separate considerations.
Defined in 2 GAGE programs, which carry 9 distinct definitions of it. The wording above is taught in Certified AI Data Governance Professional (CADGP).
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.
An AI system architecture in which a model retrieves relevant documents or data from an external store at query time and includes them in its prompt, rather than having been trained directly on that data. Removing a document from the retrieval store generally prevents its future retrieval, a retention advantage over fine-tuning, though the retrieval store's embeddings, any cached prior responses that already included the document's content, backup copies of the index, and vendor training-on-query terms remain separate considerations.
a technique in which a system searches an external document store or database at run time and incorporates relevant results into its response, without retraining the underlying model.
An architecture where a language model's response is grounded by first retrieving relevant text from a vector store (using embedding similarity search) and including that retrieved text as context for the model's generation. (see Topic 4.4)
An AI architecture where a system searches a corpus of documents for content relevant to a user's question, retrieves the most relevant results, and feeds that retrieved content to a language model as context to generate an answer.
An AI architecture where a model retrieves relevant documents from an external corpus at query time and uses them as context to generate a response, rather than relying solely on knowledge encoded during training.
Where it is taught
The exact lessons this term appears in. The first module of every program is free with a free account.
- The Eight Components I · The Agent, Deconstructed, Certified Agentic AI Governance Professional (CAAGP)
- Retention versus the model that memorized: deleting data a model already learned from · Consent, Purpose, and the Law of Data, Certified AI Data Governance Professional (CADGP)
- Embeddings are data too: the vector store as a personal-data system · Feeding the Machines, Certified AI Data Governance Professional (CADGP)
- The injectable corpus: documents that attack the AI that reads them · Poison, Leaks, and the Adversary, Certified AI Data Governance Professional (CADGP)
- Membership inference and extraction: what a model reveals about its training data · Poison, Leaks, and the Adversary, Certified AI Data Governance Professional (CADGP)
- Red-teaming your own corpus: finding the poison before the attacker uses it · Poison, Leaks, and the Adversary, Certified AI Data Governance Professional (CADGP)
- Least privilege in the estate: who can read what, and the analyst who can read everything · Access and the Keys, Certified AI Data Governance Professional (CADGP)
- The taxonomy of data incidents: breach, leak, poison, drift, and wrongful training · Data Incidents, Certified AI Data Governance Professional (CADGP)
- The deletion that was not: personal data found in a model trained last year · Data Incidents, Certified AI Data Governance Professional (CADGP)
Where it shows up at work
A definition is where RAG (Retrieval-Augmented Generation) 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.
Understands prompt injection, data poisoning, model theft, insecure integrations and excessive agent privileges, and the controls that reduce each.
3 lessons on this page teach it as a primary outcome.
Classifies information, applies least privilege, sets retention and acceptable-use rules, and controls what may enter a prompt, a log or an embedding.
3 lessons on this page teach it as a primary outcome.
Finds unapproved AI use, sets which tools are approved and what may be pasted, and detects leakage without policing every keystroke.
3 lessons 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
- AI Governance Engineerneeds it at depth · 7 open postings this week
- AI Privacy Engineerneeds it at depth · 6 open postings this week
- Cybersecurity Risk Analystneeds working knowledge
- Chief Information Officer, technology leadership editionneeds working knowledge
- Chief Information Security Officer, AI security focusneeds 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: OWASP Top 10 for LLM Applications; Google SAIF; NIST AI RMF MEASURE 2; ISO/IEC 27001.
One interview question about it
What are the security failure modes specific to AI systems, and which conventional control covers none of them?
Asked of candidates for ai security fundamentals. 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.
- Cybersecurity and IT carries it as a claim to back up: Threat modeling and control design transfer; the AI-specific threats are new.
- A computer science or software engineering degree carries it as a claim to back up: Security fundamentals are a course you took, not yet a control you ran.
- A cybersecurity or information assurance degree carries it as work you can show: Security fundamentals are your coursework and your labs.
Terms it appears with
Not an alphabetical neighbourhood: these are the terms taught in the same lessons, ranked by how often they appear together.