What is retrieval evaluation in fashion AI?
Retrieval evaluation measures how well a search or RAG system finds the right documents or products for a given question before an AI model generates an answer.
In short
Retrieval evaluation is the practice of testing whether a search component returns the right content for a query. In fashion AI it checks, for example, whether an assistant answering a buyer's question about delivery terms actually pulls the correct contract clause or product record before it writes a reply.
How does it work in practice?
A team builds a test set of realistic questions and marks which documents, products or passages are the correct answers. The retrieval system runs each query, and its results are compared with the expected ones. Common measures include whether a correct item appears in the top few results, how high it ranks and how many irrelevant items are mixed in.
Example test cases for a fashion company might include:
- Which jackets in the autumn range use recycled polyester? with the expected style numbers.
- What is the order minimum for new accounts in Italy? with the expected policy paragraph.
- Care instructions for the wool blend knit with the expected care label text.
Why does it matter for fashion businesses?
Internal assistants for sales, customer service or product teams often fail quietly. They give fluent answers that are based on the wrong season's price list or a similar but different style. Retrieval evaluation shows where the search breaks, so teams can fix data, chunking or ranking rather than endlessly rewriting prompts.
How does AI use it?
Retrieval evaluation is part of building any RAG application. Language models can also help by generating candidate test questions from product data or by judging whether retrieved passages are relevant, although such automated judgements should be spot-checked by people who know the range.
Common pitfalls
- Too few test queries. A handful of examples does not reflect how buyers and staff really ask.
- Ignoring seasonality. Test sets must be refreshed as collections, prices and policies change.
- Only testing the final answer. Without measuring retrieval separately, the root cause of errors stays hidden.
Frequently asked questions
What is the difference between retrieval evaluation and LLM evaluation?
Retrieval evaluation tests whether the right information was found. LLM evaluation tests whether the model used that information to produce a correct, useful and safe answer.
How many test questions are needed?
There is no fixed number, but the set should cover the main question types, product categories and languages the system will face. Many teams start small and grow the set from real user queries and reported errors.