AI evaluation and data labelling are the two jobs that decide what an AI model learns and whether it can be trusted. Data labelling produces the examples a model learns from, such as a question paired with its correct answer. Evaluation tests the model and scores its answers.
Every model learns from examples. A large language model (LLM) first learns from huge amounts of ordinary text. Then it is taught to follow instructions and to answer helpfully, from examples written by people. Data labelling companies hire experts, such as doctors, lawyers and translators, to write these examples. The experts also compare pairs of answers from the model and mark which one is better. This is called human feedback.
Once a model is built, someone has to judge it. LLM evaluation tools ask the model hundreds or thousands of test questions and score each answer. Here is a typical case. The test set for a Tamil customer-service bot holds 800 questions with approved answers. A new version of the bot scores 92%, and the old one scored 89%, so the new version is released. A security team uses the same tools to check that the model answers safely, for example that it never reveals another customer's details. Attacks written by an automated red team make useful test questions.
Evaluation looks at the model from outside, through its answers. AI interpretability and alignment tools look inside it. They show which internal patterns the model uses to reach an answer, adjust how it behaves and compress it so that it runs more cheaply. This is the newest of the three areas, and it is the research problem the whole field is now hiring for.
Without these jobs, a company has no way to know whether its model is correct, or why it fails. The checks that stop a harmful request while the model is answering live users belong to AI security.