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What Is Data Annotation? Types, Uses, and Its Role in AI Training
October 2, 2026
TL;DR: Data annotation for AI training does four jobs. It creates ground truth, supplies preference signals, builds benchmarks, and turns failures into feedback. As models get more capable, the work shifts from simple tagging toward expert judgment, with PhDs, engineers, and domain specialists taking on tasks that require expertise beyond general annotation.
What is data annotation? Data annotation is the process of adding information, context, or human judgment to data and model outputs so AI systems can learn from them and be evaluated against them. That includes familiar labeling tasks as well as evaluating LLMs, coding agents, and other frontier AI systems.
Here, we’ll look at the types of data annotation, the four jobs of data annotation for AI training, how it improves machine learning models, and when the work requires domain experts rather than general annotators.
Data annotation is the human work of adding information or judgment to raw data and model outputs so they can be used to train, evaluate, and improve AI systems. An annotation might be a tag, text span, ranking, score, correction, or written critique, depending on what the model needs to learn or what researchers need to measure.
Data labeling is a type of data annotation focused on assigning predefined labels or categories to data. Data annotation is broader and can also capture context, quality, preferences, corrections, and expert judgments used to train or evaluate AI models.
Take an email as an example: labeling an email as spam is data labeling. Identifying entities in the message, explaining why it violates a policy, scoring a model’s summary, or ranking two generated responses are all forms of data annotation.
The terms are often used interchangeably, especially for straightforward classification tasks. For a closer look at how labeling works across AI training workflows, see our guide to data labeling for AI models.
Generic labeling follows clear rules that do not require specialized knowledge. Expert annotation requires domain expertise to determine what a correct or high-quality answer looks like.
As models handle more routine tasks, useful annotation moves toward harder problems where the answer itself requires judgment. That is why frontier AI training increasingly relies on PhDs, engineers, linguists, and other domain experts to create, evaluate, and correct model outputs.
The main types of data annotation are text, image and video, audio, code, and sensor or multimodal annotation. Each type reflects the data being annotated, while the specific annotation can range from a simple label to an expert judgment about a model’s output.
These types describe what gets annotated.
For AI training, the more important distinction is what that annotation does for the model. It can establish ground truth, provide a preference signal, create a benchmark, or capture failure feedback.
Data annotation is used for four main jobs in AI training: establishing ground truth, providing preference signals, building benchmarks, and capturing failure feedback.
The first two jobs teach and steer the model. The last two measure and diagnose it. A training program that only invests in the first two is flying without instruments, because it has no reliable way to tell whether behavior actually improved.
SFT, RLHF, and evaluation data play different roles in AI training: SFT demonstrates desired behavior, RLHF captures human preferences, and evaluation data measures model performance.
Supervised fine-tuning (SFT) uses expert-written or corrected examples to show a model what a strong response looks like.
Reinforcement learning from human feedback (RLHF) turns comparisons between model outputs into preference data used to steer behavior.
Evaluation data tests performance against trusted tasks, rubrics, or outcomes rather than teaching the model directly. For frontier models, HLE experts can create and verify evaluations difficult enough to expose remaining capability gaps.
Data annotation improves machine learning models by giving teams ground truth, preference signals, benchmarks, and failure feedback they can use to train, evaluate, and correct model behavior.
In supervised training, annotated outputs define what the model should learn. If those targets are inaccurate or ambiguous, the model learns from the wrong examples.
The LIMA paper shows how much careful data selection can matter. Meta AI researchers fine-tuned a 65B-parameter model on just 1,000 curated prompts and responses, with human evaluators preferring its responses or rating them equivalent to GPT-4’s in 43% of cases.
More examples increase volume, while better examples shape what the model actually learns.
Two responses can both be factually correct while differing in instruction following, reasoning, or safety. Preference annotation captures those differences by asking annotators to compare or rank outputs against defined criteria, providing information a correct/incorrect label cannot.
Benchmarks use verified tasks, reference answers, and scoring criteria to measure model capabilities. If a task is ambiguous, an answer is wrong, or the rubric rewards the wrong behavior, the result can misrepresent performance. A benchmark is only as reliable as the judgments behind it.
A pass/fail result shows that a model failed. Detailed annotation shows why, whether it ignored a constraint, used the wrong formula, invented a citation, or followed invalid reasoning.
That diagnosis helps teams decide what to fix next. Red teaming extends the process to adversarial and unexpected cases, turning model failures into feedback for the next training or evaluation cycle.
Data annotation varies by domain and task. It can mean ranking an LLM response, evaluating pronunciation, verifying code or scientific reasoning, or reviewing whether a robot completed an action correctly.
Text and LLM annotation includes writing ideal responses, ranking outputs, checking factual accuracy, and scoring qualities such as tone or safety. A native-speaking linguist might flag an accurate Spanish response because its phrasing sounds translated rather than natural to someone in Mexico, showing how language expertise shapes better models.
Audio annotation can assess transcription, pronunciation, prosody, and naturalness. As our guide to how native speakers train audio AI to sound human explains, a Brazilian Portuguese speaker catches stress patterns, vowel reduction, or regional cadence that a general fluency check misses.
Coding annotation includes reviewing code, writing tests, validating environments, and evaluating agent outcomes. In Athyna Intelligence’s work building agentic coding tasks for Terminal-Bench 2, engineers created tasks humans could solve reliably while frontier models failed, including one verified task with a pass@10 of 0.
STEM annotation can require experts to author problems, verify solutions, and evaluate model reasoning. While building STEM tasks that challenge frontier models, Athyna Intelligence found that accepted tasks took PhD researchers more than a working day each, with scientific inputs traced to peer-reviewed literature.
Physical AI annotation connects perception with action by reviewing demonstrations, trajectories, sensor data, and task outcomes. Hiring for physical AI training depends on the task, since evaluating teleoperation data can require different expertise from reviewing simulation outputs or robotics safety cases.
Good data annotation is consistent, verifiable, and traceable, and it comes from annotators qualified to make the judgment the task requires. Strong annotation programs typically include:
Agreement alone does not guarantee quality. Two annotators can consistently reach the same wrong conclusion. For specialized AI training tasks, quality depends on both consistent annotation and whether the annotators have the expertise to judge the task correctly.
Data annotation is typically done by crowd workers, trained generalists, or domain experts. Crowd workers handle high-volume tasks with clear answers, while experts are needed when correctness depends on specialized knowledge in areas like coding, science, law, or language.
In frontier AI training, experts may work as trainers or evaluators. Trainers create and improve training data, while evaluators assess model outputs against defined criteria. The roles often overlap, as our guide to what an AI trainer does explains.
Data annotation costs depend on complexity, expertise, and volume. Simple labeling may be priced per item, while expert work costs more: in the US, AI trainers have a median total pay of about $80,000 a year, according to Glassdoor. Cost also varies by how AI training data is sourced, especially when projects require expert-generated data, review, and validation.
Location changes the math most. Based on Athyna Intelligence pricing data, LATAM PhD-level researchers cost 40% to 60% less than equivalent US or European hires. The average AI model training specialist in the US earns around $120,000 a year, while comparable roles in Latin America average around $40,000.
Yes. Synthetic data shifts human annotation toward verification, evaluation, and adjudication rather than eliminating it. Models can generate data at scale, but research published in Nature found that indiscriminate training on model-generated content can lead to model collapse.
A model might generate thousands of physics problems, for example, but an expert still needs to verify the solutions, catch ambiguous questions, and determine which problems are useful for training or evaluation.
Athyna Intelligence connects AI labs with vetted PhDs, engineers, linguists, and other domain experts to generate, review, and evaluate AI training data.
Each project is matched to the expertise the task requires, whether that means an engineer who can reproduce failures and verify fixes for a coding evaluation or a linguist who can catch regional language and cross-lingual nuances in multilingual training.
Have an AI training project that requires specialized expertise? Talk to us today.
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Data annotation is used to establish ground truth, provide preference signals, build benchmarks, and capture failure feedback. These signals help teams train models, steer their behavior, measure performance, and identify what needs improvement.
Data annotation improves machine learning models by defining desired outputs, capturing human preferences, creating reliable evaluation criteria, and identifying why models fail. The quality of those annotations directly affects what models learn and how accurately their performance can be measured.
The main types of data annotation include text, image and video, audio, code, and sensor or multimodal annotation. The right type depends on the data and the AI system being trained or evaluated.
Data labeling assigns predefined categories or labels to data. Data annotation is broader and can also include rankings, corrections, scores, critiques, and expert judgments about model outputs.
Frontier models increasingly work on tasks where determining the correct answer requires specialized knowledge. Engineers, PhDs, linguists, and other domain experts can evaluate reasoning, correctness, and edge cases that general annotators may not be qualified to judge.
Yes. Synthetic data can increase volume, but human experts are still needed to verify outputs, evaluate quality, resolve ambiguous cases, and determine whether generated data is suitable for training or evaluation.
Data annotation costs vary by task complexity, required expertise, volume, and quality controls. Simple labeling may be priced per item, while expert annotation for coding, STEM, legal, or other specialized AI training tasks is often priced by time or task.
