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Data annotation

As explained in LLMs in Production

Data annotation is the process of attaching labels or metadata to data instances, often with awareness of their positions or spans.

A large text dump may have no labels or annotations. Data annotation attaches labels or metadata to data instances, but next-word or masked-word training can use self-supervised methods without it. Skipping annotation does not mean skipping data preparation. section 4.3.2

Before using an unannotated dataset:

  • Define a schema and normalize or scale features for the intended outcome. section 4.3.2
  • Check bias and anomalies with informed verification, not automated checks alone. section 4.3.2
  • Convert the data into a model-ready format; for LLMs, this involves tokenization and embedding. section 4.3.2

How LLMs in Production covers it

How deep it goes
Explains why annotations may be absent, then gives high-level preparation steps without detailing annotation workflows or data pipelines.
Examples
Uses large Parquet or text dumps with no labels or annotations as the concrete case.
What you can do after
Evaluate/understand whether missing annotations matter for an LLM training task without mistaking them for proof of data quality.
How it connects
Connects annotation to self-supervised learning, data cleaning, schema design, bias checks, tokenization, and embeddings.
Cover of LLMs in Production
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