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 and images, writing reference answers, and comparing responses generated by the model itself. In this article I explain what these jobs involve, how they connect to model training, and what separates a valuable annotator from an expendable one.
TL;DR
- Annotating data means producing the correct answer a model needs to learn: labels, transcriptions, reference answers, and comparisons between outputs.
- Reviewing AI responses means fact-checking, scoring against a rubric, and picking the better of two responses with a written justification; those preferences train the model.
- Platforms measure you with control tasks and inter-annotator agreement; judgment and specialization (code, math, law) are worth more than speed.
What data annotation is and why it exists as a job
A machine learning model learns from examples. For it to tell spam from legitimate email, someone marked thousands of emails as "spam" or "not spam". For a self-driving car to recognize pedestrians, someone drew boxes around pedestrians across millions of frames. That "someone" is the data annotator, and their work product is the label: the piece of data that tells the model what the correct answer was.
This exists as paid work because models need enormous volumes of correct examples, and producing a correct example requires human judgment. The industry has been operating for over a decade in computer vision and speech, but LLMs changed the profile of the task: it's no longer just about assigning categories, but about writing and judging text. That's where the roles now advertised as "AI trainer" or "response evaluator" come from. The most common task types:
| Task type | What it involves | Concrete example |
|---|---|---|
| Classification and labeling | Assigning categories to text, images, or audio | Marking whether a comment is spam, toxic, or neutral |
| Visual annotation | Drawing boxes and polygons over images or video | Outlining pedestrians and signs for autonomous driving |
| Transcription and speech | Turning audio into text, tagging speakers and noise | Transcribing calls to train speech recognition |
| Demonstrations | Writing the ideal answer to a prompt | Drafting the best possible explanation of a concept |
| Response comparison | Picking the best among two or more model outputs | A vs B with a written justification |
| Rubric-based evaluation | Scoring a response across several dimensions | Correctness, format, and safety, each on its own scale |
| Abuse testing (red teaming) | Trying to make the model fail or produce harmful content | Finding prompts that break the model's policies |
The first three rows are classic annotation: closed instructions and high volume. The last four grew with LLMs and demand careful reading, fact-checking, and writing; that's why they pay better and are assigned through stricter filters.
Keep reading
That is the first half. The full walkthrough — with the rest of the implementation, the trade-offs and the things that only show up in production — is on my blog:
Read the full post on ramonchancay.me →
Originally published at www.ramonchancay.me/blog/data-annotation-jobs-reviewing-ai-responses.
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