# Experts in-the-Loop at Stitch Fix

DevFeed: [Experts in-the-Loop at Stitch Fix](<https://devfeed.tech/articles/experts-in-the-loop-at-stitch-fix-29337.md>)

Original publisher: [Read original article](<https://multithreaded.stitchfix.com/blog/2022/09/02/stylists-in-the-loop/>)

Published: 2022-09-02T09:00:00Z

Content type: article

Language: en

Sources: [Stitch Fix](<https://devfeed.tech/sources/stitch-fix.md>)

Topics: [personalization](<https://devfeed.tech/topics/personalization.md>), [recommendations](<https://devfeed.tech/topics/recommendations.md>), [datasets](<https://devfeed.tech/topics/datasets.md>), [Training AI Models](<https://devfeed.tech/topics/training-ai-models.md>)

Tags: [customer](<https://devfeed.tech/tags/customer.md>), [datasets](<https://devfeed.tech/tags/datasets.md>), [e-commerce](<https://devfeed.tech/tags/e-commerce.md>), [personalization](<https://devfeed.tech/tags/personalization.md>), [recommendations](<https://devfeed.tech/tags/recommendations.md>), [training](<https://devfeed.tech/tags/training.md>), [training-data](<https://devfeed.tech/tags/training-data.md>)

## AI overview

Stitch Fix describes how human stylists help create high-quality training data for personalized outfit recommendations in its Freestyle direct-shopping experience. The article explains why stylist judgments are needed to assess recommendation quality and client experience at scale.

## Source excerpt

Imagine your job is to personalize search results on an e-commerce site for returning customers, classify the presence or absence of pedestrians in street photos, or develop an app that translates languages. In all of these cases, a basic ingredient is a dataset of annotations provided by a human. For any company seeking to personalize experience for its customers, combining human computation with algorithmic computation is essential. This is also true for Stitch Fix. At Stitch Fix, we recently launched Stitch Fix Freestyle, our direct-shopping experience, where our algorithmic recommendations are now directly shared with clients in their own personal shopping feed - a different approach from our original Fix experience, where a team of expert stylists determined what should go in the client's Fix. Central to the Freestyle experience for clients is showing individual items as part of complete outfits, where both items and outfits are personalized based on our clients unique size, fit, style, and price preferences. But, what makes a good outfit? And, how do we balance personalization to a customer with a given level of outfit quality or a particular stylistic slant? In order to bootstrap a new product like this, to offer personalized outfit recommendations at scale to our around 4m clients, we need some high-quality training data. In particular, we need data specific to what it means to have a good client experience - or what makes a good outfit for a given client at Stitch Fix. Datasets like this aren't exactly floating around: assembling them requires an intentional, large-scale effort. The best way to get high-quality data sets is to work with our in-house experts: our stylists. In this post, you'll learn more about the purpose and impact of stylist-in-the-loop projects, and the powerful impact that our stylists have in building the future of personalized shopping. Bringing in our "experts in-the-loop" Working with our expert stylists helps us improve our clients'