LLM Training Data

What is LLM Training Data?
LLM training data is the collection of text and structured datasets used to train or fine-tune large language models. General-purpose models train on broad web-scale corpora; domain models and fine-tunes add curated, task-specific datasets — for commerce, that means product catalogs, price histories, reviews, and matched competitor offers.

Dataset quality directly bounds model quality. For e-commerce AI, the valuable properties are structure (clean fields rather than raw HTML), freshness (prices and availability dated and versioned), coverage (marketplaces, regions, and categories relevant to the task), and licensing clarity. Deduplication and correct product matching prevent a model from learning contradictory facts about the same item.

Teams typically combine approaches: fine-tuning on historical commerce datasets teaches a model the domain's patterns, while RAG over a live data feed supplies the current facts at inference time.