What is training data?

A model knows nothing it wasn’t shown. Training data is everything it was shown: the raw material from which machine learning extracts patterns. For a large language model, that means trillions of tokens of text and code. For an image model, billions of images paired with captions.

Where it comes from

• Web crawls: large scrapes of public web pages (Common Crawl is a widely used public dataset).
• Books, papers and code: digitized books, academic articles, public code repositories.
• Licensed data: deals with publishers, stock-image libraries, forums and news organizations.
• Human-created examples: contractors writing ideal answers and ranking outputs for fine-tuning and alignment.
• Synthetic data: examples generated by other AI models, increasingly common.

Training happens in stages: broad pre-training on massive data to learn language and world knowledge, then smaller, carefully curated datasets to teach the model to follow instructions and behave helpfully.

An analogy for the meatbags

Training data is a child’s upbringing compressed into months. Raise a child exclusively on tabloids and they will have a very particular view of the world. Raise a model on the open internet and it absorbs brilliance, errors, biases and spam in roughly the proportions the internet contains them — which is why curation matters as much as volume.

Why it matters for the Chosen

• Knowledge cutoff: models don’t know what happened after their data ends. For current facts, use tools connected to search, or feed in documents via RAG.
• Bias and gaps: what’s rare in the data, the model is bad at. Niche industries, minority languages and local knowledge are weak spots — and opportunities for people who supply that expertise.
• Your data as an asset: a business’s own data — support tickets, sales calls, product docs — is training data nobody else has. Using it to fine-tune models or power retrieval is a real competitive advantage.
• Privacy: read the data-use settings before pasting client information into any AI tool.
• Legal risk: copyright disputes over training data are unresolved. If you sell AI-generated work commercially, know your tool’s terms and any indemnities offered.

Examples

• An LLM that writes fluent Python because public code repositories were in its training data.
• An image generator that struggles with a particular object because few labeled images of it existed.
• A customer-support model fine-tuned on a company’s past ticket resolutions.

Garbage in, garbage out. Humanity in, humanity out — which is, depending on the day, the same thing.