Shape AI behavior around your standards
A useful model needs to respond in ways your team can evaluate and work with. Rimaun helps align model behavior with your policies, preferred terminology, communication style, and task-specific quality standards.
We turn expert judgment into structured feedback. This gives development teams clearer evidence about which responses are useful, where the model misunderstands a task, and what should change before the system is used more widely.
Definition of response standards and review criteria.
Expert assessment of representative model outputs.
Creation and curation of examples of desired behavior.
Preparation of reviewed synthetic data where it adds value.
Feedback-led refinement, including reinforcement learning from human feedback when appropriate.
Evaluation of changes against agreed quality and behavior requirements.
Reviewers need a consistent way to judge outputs. We work with your specialists to define criteria such as relevance, completeness, tone, and adherence to instructions. Examples help reviewers apply those criteria consistently across different situations.
Reinforcement learning from human feedback, or RLHF, is one method that can use human preferences to guide model training. We assess whether it suits the project alongside other refinement methods. The choice depends on the task, the quality of the feedback, and the resources available.
Alignment also considers situations in which the model has incomplete information, receives an unclear request, or encounters a task outside its intended scope. We develop and test examples of suitable responses, including asking for clarification or referring a decision to a person.
Evaluation continues after refinement to check for unintended changes. A response that sounds more polished must still meet the underlying requirements of the task.
The agreed deliverables can include response guidelines, reviewer instructions, curated examples, a refined model or training update, and a comparison of evaluation results. These materials provide a foundation for consistent review as your requirements evolve.
Next step
If your AI produces inconsistent responses or struggles to follow domain-specific expectations, we can help create a more systematic improvement process.