Data annotation jobs: what labeling and reviewing AI responses involves
I've spent the past several months working as an AI annotator, reviewing model responses to train them, while also building products with LLMs. That lets me see the process from both sides: as someone who produces the training data and as someone who consumes the resulting model. Data annotation is exactly that: the work of producing the examples a model is trained and evaluated on—labeling text…
I have been an AI annotator for several months now, working on both ends of the process: creating training data and consuming the models' outputs. Data annotation is the act of producing examples a model learns from—labeling text and images, writing reference answers, and comparing responses generated by the model. In this piece, I will explain what these jobs entail, their connection to model training, and what distinguishes a good annotator from an average one.
Annotation tasks fall into a few categories. Classification and labeling involve assigning categories to text, images, or audio. For instance, labeling emails as spam or not spam helps a machine learning model distinguish between the two. Visual annotation requires drawing boxes and polygons around objects in images or video, such as pedestrains and signs for autonomous driving. Transcription and speech tasks entail turning audio into text, tagging speakers, and identifying background noise.
Writing the ideal answer to a prompt is another common task, where annotators draft the best possible explanation or answer to a concept. Response comparison is also crucial, as it involves evaluating and selecting the best among two or more model outputs, with a written justification for the preference. Rubric-based evaluation scores responses across various dimensions, such as correctness, format, and safety, each on its own scale.
More recently, with the rise of large language models (LLMs), other types of tasks have emerged. These include abuse testing (red teaming), where the goal is to make the model fail or produce harmful content by finding prompts that break its policies. These tasks require a higher level of judgment and specialization, such as knowledge of code, math, or law, and are paid more than the earlier tasks.
Platforms measure annotators using control tasks and inter-annotator agreement. In other words, they assess the annotator's judgment through repeated tasks and compare the results to ensure consistency. Annotators who can demonstrate effective and accurate judgment, backed by specialization, tend to perform better and earn higher pay.
Written by urgent.news from Dev.to's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
