CODE

A simple technique can make your vector search 10x cheaper. And you probably haven't heard of it yet. Consider this: A user searches "runnng shoes" (yes, misspelled) looking for size 10, within 10 miles, under $100. Vector search runs on 500 products, then the filters apply – and only 12 match the size, location and price. That's 500 similarity calculations to surface 12 results. And if the typo didn't get caught? Those 12 might not even include what the user wanted. Standard pre-filters would return ZERO results for "runnng" – it's not an exact match. Post-filtering catches the typo semantically but wastes compute on 488 irrelevant products first. This is how search pipelines typically work. Most teams have accepted this as normal – run vector search first to get semantically relevant results, then apply filters afterward. Standard vector search does support basic pre-filters (like "price < $100" or "size = 10"), but those filters are rigid, only handling exact matches and simple comparisons. They can't handle typos, wildcards, or complex text analysis. So you're stuck: use exact-match pre-filters and get zero results for typos, or post-filter massive datasets and waste compute. What you actually need is filtering that handles precision and fuzziness together – precise enough for "size 10" and "under $100," flexible enough to match "runnng to running," and smart enough to handle complex geospatial queries like "within 10 miles." And it needs to happen before vector search runs, not after. But the bigger point is this: – Post-filter: search everything, hope for the best.
– Pre-filter with lexical intelligence: search only what matters, get it right. Precision and semantics work better as layers than as tradeoffs. Now that you see the problem, let me show you what the fix actually looks like in practice 👇 This is ex [Translated from EN to English]

→ View original post on X — @akshay_pachaar, 2026-03-30 07:36 UTC