Artificial intelligence instruction following measures whether a model acts on the task that was actually requested. It includes respecting format, scope, constraints, priorities and explicit requirements rather than merely producing related content.
Reliable adherence matters in real workflows because missed requirements create review and repair work. Evaluation should test competing instructions, long contexts and tool use while distinguishing valid user directions from unsafe or unauthorized requests.
Acronyms and aliases
model instruction adherence synonymAI instruction following variantartificial intelligence instruction following variant
Related terms
Frequently asked questions
How is AI instruction following evaluated?
Tests check whether outputs satisfy stated requirements, formatting, constraints and priorities across representative tasks.
Should a model follow every instruction?
No. It should follow valid authorized instructions while respecting higher-priority safety, privacy and permission boundaries.
