Domain knowledge explains what matters in a workflow beyond its visible steps. It includes terminology, exceptions, quality standards and the reasoning experienced people use to distinguish a useful result from a merely plausible one.
Artificial intelligence automation becomes more dependable when this knowledge is mapped before implementation. Instructions, examples, references and tests can capture parts of it, while accountable experts remain necessary for ambiguity and changing real-world conditions.
Acronyms and aliases
subject-matter knowledge synonym
Related terms
Frequently asked questions
Why do artificial intelligence workflows need domain knowledge?
Without specialized context, a model may produce fluent output that misses business rules, exceptions or the quality standard that makes the result useful.
How can a team capture domain knowledge for an agent?
It can document decisions, rules, examples, reference sources and review criteria and test them against representative real tasks.
