As a multilingual content designer working at the intersection of AI, UX, and languages, I’ve created this intent annotation guide to bring clarity and consistency to team workflows — especially for early-stage or scaling LLM projects.
It’s a decision-making framework, built to help annotation teams work faster and smarter — even when user intent is ambiguous, emotional, or sarcastically phrased.
If you're looking for a contributor who can annotate, systematize, and document at the same time — from junior training to senior-level edge cases — this guide shows how I think and work.
This guide was designed with precision, structure, and UX-awareness — qualities that are essential for scalable annotation workflows.
It addresses real-world edge cases, reduces variability in labeling, and makes it easier for new team members to get up to speed.
I understand how the model “thinks” — and what annotators need to do to help it learn accurately, efficiently, and without bias.
If consistent data quality, predictable annotation decisions, and smooth onboarding without overloading your managers matter to you — then you're already holding the result of my work.