Glossary · E-commerce ML
LLM-Powered Catalog Enrichment
also: LLM catalog enrichment · catalog enrichment · AI catalog enrichment
Definition
LLM-powered catalog enrichment uses large language models to generate product descriptions, attributes, categorization, and structured data from sparse inputs (SKU name, supplier feed) at scale. It eliminates the manual-curation bottleneck that has historically limited catalog coverage in marketplace and retail businesses.
Product catalogs at scale have the long-tail problem: the top 20% of SKUs have rich descriptions, the bottom 80% are sparse. LLMs fill the gap — fine-tuned on the retailer's style guide and attribute schema, they generate consistent descriptions, extract structured attributes (color, material, compatibility), and suggest categorization. The hard problems are evaluation (retrieval-based ground truth), hallucination suppression (retrieval-augmented generation), and measurement of downstream lift (click-through and conversion improvement).
Essays on this concept
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