---
title: "Understanding Etsy’s Vast Inventory with LLMs"
description: "Etsy’s seller-created inventory spans more than 100 million items and thousands of categories, making it difficult to capture attributes such as color, material, or niche properties without global SKUs. Because sellers often provide descriptions, titles, and photos instead of complete structured fields, Etsy uses LLMs to distill unstructured listing data into attributes for search filters, comparisons, and other experiences. The pipeline combines seller data, expert-selected few-shot examples, taxonomy logic, and category rules; parallel prompts are routed through LiteLLM, then responses are validated with Pydantic dataclasses and monitored with tracing, metrics, alerts, and performance checks. Human-reviewed silver labels support evaluation, while target categories increased complete attribute coverage from 31% to 91%, and inferred attributes improved search-filter engagement and overall post-click conversion."
---

# Understanding Etsy’s Vast Inventory with LLMs

[Etsy](https://yomu.fyi/company/etsy) · Vipul Setty · Oct 13, 2025

**Type:** Problem & solution

## Summary

Etsy’s seller-created inventory spans more than 100 million items and thousands of categories, making it difficult to capture attributes such as color, material, or niche properties without global SKUs. Because sellers often provide descriptions, titles, and photos instead of complete structured fields, Etsy uses LLMs to distill unstructured listing data into attributes for search filters, comparisons, and other experiences. The pipeline combines seller data, expert-selected few-shot examples, taxonomy logic, and category rules; parallel prompts are routed through LiteLLM, then responses are validated with Pydantic dataclasses and monitored with tracing, metrics, alerts, and performance checks. Human-reviewed silver labels support evaluation, while target categories increased complete attribute coverage from 31% to 91%, and inferred attributes improved search-filter engagement and overall post-click conversion.

## Context

Etsy’s inventory includes millions of unique, handmade, or customized items, often without global SKUs or complete structured attributes. Sellers commonly provide useful information only in titles, descriptions, and photos, but processing that unstructured data for every search query creates latency concerns and makes consistent attribute extraction difficult.

## Approach / What changed

Etsy uses LLMs to extract structured product attributes from seller-provided text and images. The pipeline combines taxonomy logic, category-specific rules, expert-selected few-shot examples, parallel prompts routed through LiteLLM, Pydantic validation, post-processing, and exports to filestores, database tables, and search. Evaluation uses human-reviewed silver labels and monitored performance checks.

## Takeaways

- Human-annotated datasets were evaluated with precision, recall, and Jaccard index, but annotator errors, time, and cost led Etsy to use LLM-generated silver labels reviewed by domain experts.
- Each listing’s JSON context combines seller data, few-shot examples, product-taxonomy logic, and category rules; parallel LLM responses are parsed into Pydantic dataclasses for type and business-rule validation.
- Inference errors are logged and surfaced through metrics and alerts, while sampled traces support debugging and sample-based performance checks can terminate the pipeline when metrics deviate significantly from baseline.

**Tags:** [LLMs](https://yomu.fyi/topic/llm), [Model Evaluation](https://yomu.fyi/topic/model-evaluation), [Search](https://yomu.fyi/topic/search)

- Source: [Etsy](https://www.etsy.com/codeascraft/understanding-etsyas-vast-inventory-with-llms)
- Source URL: https://www.etsy.com/codeascraft/understanding-etsyas-vast-inventory-with-llms
- Ingested by Yomu: 2026-09-09T16:04:59.055Z

[Read original post](https://www.etsy.com/codeascraft/understanding-etsyas-vast-inventory-with-llms)