Six Lessons From Building an AI-Powered Marketplace Search Engine
A builder’s postmortem on multilingual AI marketplace search, from fake category IDs and broken price filters to caching, regex bugs, and latency.
Building an AI-powered marketplace search engine has taught several valuable lessons. First, a price filter that silently fails looks identical to one that works properly, highlighting the importance of asserting filter functionality through absurd values. Second, model-generated category IDs that appear valid can still be wrong, requiring validation against an authoritative list to avoid quietly incorrect results.
Third, substring matches in queries, which were misusing the "new" substring in "newborn baby clothes", necessitate whole-word boundaries to prevent misinterpretation. Fourth, latency costs more than relevance, as cold searches taking several seconds before rendering results drove most users to abandon the platform. Optimizing latency by caching searches for 24 hours dramatically improved conversion rates.
Lastly, image search is accurate about categories but unreliable in matching exact items, suggesting a trade-off between accuracy and usefulness that should be considered in product development.
Written by urgent.news from HackerNoon's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
