Labeling

The previous article, Annotation, covered the broad process of marking up raw data with correct answers — everything from drawing bounding boxes around cars to transcribing audio. Labeling is closely related, and the two words are often used interchangeably in casual conversation. This article draws out the distinction worth knowing, and then focuses on something the last article only touched on: the actual, practical techniques teams use to get labels assigned at real-world scale.

The simple definition

Labeling is the specific act of assigning a discrete category or value — a label, as covered in the Label article — to a piece of data. Where “annotation” is the broader umbrella covering many kinds of markup (bounding boxes, transcriptions, segmentation masks, and more), “labeling” more precisely refers to the narrower, extremely common case of attaching a defined tag: “spam” or “not spam,” “cat” or “dog,” “positive” or “negative.” Every labeling task is a form of annotation; not every annotation task is quite this simple a labeling task.

In practice, most people use the two words interchangeably, and that’s fine for everyday conversation — but it’s worth knowing the distinction exists.

From task definition to usable labels

Suppose a team wants to classify support tickets:

billing
technical_problem
account_access
other

Before anyone labels data, the team must define each category. Does “I was charged but cannot log in” belong to billing, account access, both, or the main reason selected by a rule?

Labeling is not merely clicking buttons. It is a designed process for producing consistent answers.

Labeling ten examples

Suppose ten tickets are independently labeled by two people:

Agreed labels:    8
Disagreements:    2
Raw agreement: 8 / 10 = 80%

The disagreements reveal unclear examples or unclear instructions. The team can review them, improve guidelines, and adjudicate the final labels.

The labeling pipeline

flowchart LR
    A[Choose target] --> B[Define label set]
    B --> C[Write examples and rules]
    C --> D[Pilot small batch]
    D --> E[Review quality]
    E --> F[Label larger dataset]
    F --> G[Audit and version]

Starting with a pilot prevents thousands of examples from being labeled using a confusing design.

Human, programmatic, and model-assisted labeling

Human labeling: People judge each example.
Expert labeling: Specialists handle tasks requiring domain knowledge.
Programmatic labeling: Rules produce approximate labels at scale.
Model-assisted labeling: A model suggests answers that people verify.
Active learning: The system prioritizes uncertain or informative examples for review.

Faster methods introduce their own errors. Model suggestions can also anchor reviewers toward the model’s mistake, so independent checks remain valuable.

Label design mistakes

Categories overlap without a tie-breaking rule.
Other becomes a large, meaningless bucket.
Labels represent an easy proxy instead of the real goal.
Annotators lack the context needed to decide.
Sensitive or harmful content is handled without safeguards.
Quality is measured only after the full dataset is finished.
Historical outcomes are copied without examining bias.

Why the how of labeling deserves its own close look

Once you know you need labels, the real practical question becomes: how do you actually get millions of examples labeled, accurately, without it taking forever or costing a fortune? This is a genuine engineering and operations problem, and several distinct strategies have emerged to solve it, each with real trade-offs.

Crowdsourced labeling

The most common approach for large datasets is spreading the work across many people, often through a dedicated platform. A simple beginner example: a small startup building a plant-identification app might use a crowdsourcing platform to pay a few hundred people a small fee each to label thousands of plant photos with the correct species name.

At a much larger, production scale, this is exactly the business built by companies like Scale AI and Surge AI, introduced in the Annotation article — coordinating thousands of human labelers to work through massive volumes of images, text, and audio for major AI labs.

flowchart LR
    A[Raw unlabeled data] --> B[Split into small batches]
    B --> C[Sent to many crowdsourced labelers]
    C --> D[Multiple labels per example, compared]
    D --> E[Disagreements reviewed]
    E --> F[Final labeled dataset]

Crowdsourcing scales well, but quality control becomes essential — which is why serious labeling operations, as mentioned in the Annotation article, typically have multiple people label the same example and compare results, resolving disagreements through review rather than trusting any single person’s judgment.

Weak supervision and programmatic labeling

Human labeling at scale is expensive, so an important alternative technique has emerged: writing simple rules or heuristics that automatically generate approximate, “noisy” labels instead of relying entirely on human review.

For example, a team building a spam classifier might write a rule like “if an email contains three or more of these specific spam-associated phrases, label it spam” — not perfectly accurate, but useful for quickly generating a large volume of rough labels, which a model can then learn from even though some of those labels are wrong.

This technique, sometimes called weak supervision, trades some label accuracy for a dramatic increase in labeling speed and volume, and is a genuinely useful middle ground between fully manual human labeling and having no labels at all.

Active learning: letting the model help decide what to label next

A more sophisticated technique flips the usual order slightly. Rather than labeling an entire dataset upfront, an already-partially-trained model is used to identify which specific unlabeled examples it’s most uncertain about — the cases where its predictions are closest to a coin flip.

Those uncertain examples are prioritized for human labeling next, since they’re the ones most likely to teach the model something genuinely new, while examples the model already handles confidently are labeled last, or sometimes not at all. This technique, called active learning, can substantially reduce the total amount of human labeling needed to reach a given level of model performance, since effort gets concentrated exactly where it does the most good.

ANALOGY vs. TECHNICAL REALITY

Analogy: Think of a language tutor who, instead of drilling a student on vocabulary they already know well, pays close attention to which specific words the student keeps getting wrong, and focuses future practice sessions on exactly those weak spots.

Where this breaks down: A tutor makes this judgment through genuine understanding of the student’s mind. Active learning makes this judgment purely numerically — using the model’s own output probabilities (recall the softmax-based confidence scores from the Prediction article) to flag the examples where the model’s prediction sits closest to 50/50, with no actual comprehension of why those examples are hard, just a mathematical signal that they are.

Where this shows up in modern, large-scale AI systems

Labeling at the scale required by today’s large language models has taken on new forms beyond simple category tagging.

A large share of the labeling work behind models like GPT, Gemini, and Claude today isn’t “cat vs. dog” style categorization at all — it’s human evaluators reading pairs of AI-generated responses and labeling which one is better, safer, or more helpful, feeding directly into the reinforcement learning from human feedback (RLHF) process mentioned in the Annotation article.

This is a meaningfully different, more subjective kind of labeling task than the classic image or spam-classification examples, and it’s a large part of why companies like Surge AI, whose publicly listed clients include OpenAI, Microsoft, Meta, and Anthropic, have grown so significantly alongside the rise of large language models.

Key terms

Labeling: The process of assigning target answers to examples.
Label taxonomy: The defined set and hierarchy of allowed labels.
Pilot batch: Small early set used to test the labeling design.
Adjudication: Resolving disagreements using an agreed process.
Active learning: Selecting valuable examples for labeling using model behavior.

Check your understanding

Are label and labeling the same? No. A label is an answer; labeling is the process that creates or assigns it.

Should a team label the entire dataset before checking agreement? No. A pilot should reveal ambiguity early.

Common misconception

A common beginner assumption is that labeling is a one-time, upfront task completed before “the real work” of building a model begins. In reality, as active learning shows, labeling and model development are often intertwined and iterative — early rounds of labeling produce an initial model, whose weaknesses then guide which examples get labeled next, in a repeated cycle. Treating labeling as a single, isolated first step, rather than an ongoing part of the development process, is a common way projects underestimate both the true cost and the true strategic value of good labeling.

Model-assisted does not mean model-approved

A model can pre-label easy examples and send uncertain examples to people, reducing repetitive work. Its suggestions must still be checked against human-reviewed samples because accepting every suggestion can copy the model’s existing mistakes into the next training dataset.

The safe loop is: define the task, write guidelines, label a pilot set, measure agreement, review disagreements, improve the guidelines, label at scale, audit samples, and version the resulting dataset.

Where this fits in what comes next

Labeling completes the picture of how ground truth actually gets attached to raw data, whether through crowdsourcing, weak supervision, or active learning. The final article in this phase, Data Augmentation, covers a different but related challenge: what to do when you simply don’t have enough labeled examples to begin with, and how to artificially expand a dataset’s effective size without needing to label more raw data from scratch.

In one sentence

Labeling is the practical work of assigning correct-answer tags to data, and the specific technique used — crowdsourcing, weak supervision, or active learning — is often a deliberate engineering trade-off between labeling cost, speed, and accuracy, not a one-size-fits-all process.

Author: TechByteByByte Editorial Team
Reviewed by: TechByteByByte Admin
Published: August 31, 2026
Last reviewed: August 31, 2026