This section brings together real-world cases and annotation methodologies designed to address three of the most critical challenges faced by AI teams:

– How to train models to truly understand text – How to minimize the risk of annotation errors – How to onboard new annotators quickly and effectively

These tools and frameworks have been applied in projects focused on explainability, UX, cultural sensitivity, and multilingual NLP.

Intent annotation guide (EN, FR, RU)

Thinking Like a Model: A UX-oriented framework for annotation and training LLMs