Technical guide · direct answer · primary references
Generative AI governance
Practical controls to reduce hallucinations, unsafe responses and silent changes.
Direct answerGenerative AI governance combines clear scope, traceable sources, continuous evaluation and human review proportional to risk. A good system records what it answered, with which context and under which version.
Governance starts with scope
Before choosing a model, define what the application may do, which data it may access and which decisions it must not make. Explicit boundaries make testing and accountability clearer.
Control layers
- Input and output policies for sensitive content.
- Retrieval from approved sources with contextual evidence.
- Automated evaluations and human sampling.
- Versioning of prompts, models and knowledge bases.
Continuous evaluation
A representative test set should run before and after changes. Metrics should cover source adherence, completeness, safety and the ability to acknowledge insufficient information.
Frequently asked questions
Does RAG eliminate hallucinations?
No. RAG improves access to context but still requires evaluation, response policies and uncertainty handling.
Does every use require human review?
Not necessarily. Review levels should follow decision impact and reversibility.