Shopify's semantic search made your results worse? Take back control

What did Shopify change about search relevance?

It changed how a query becomes results. Older search matched the words in the query against the words in your titles, descriptions, and tags. Semantic search matches on meaning, using a model that decides which products are "about" the query even when the words do not line up.

Meaning-based matching can help. It can connect "running shoes" to a product titled "trail trainers" with no shared word. But it can also drift. A query for an exact model number or a precise term comes back with products the model thinks are related and you know are not. That is what felt "irrelevant or inaccurate" for queries that used to work. The problem was never that semantic matching exists. It arrived as the only option, with no dial and no off switch.

That is the enemy. A relevance change you did not choose, with no way to choose back. A black box where your search bar used to be.

What control does native Shopify give you over search mode?

Little to none on the matching itself. Search and Discovery lets you manage synonyms, set some filters, and a few rules, but the core relevance mode is the platform's call, not yours. There is no toggle to run plain keyword matching, and no slider for how aggressive the semantic layer should be.

So the native workaround is to push against the model with synonyms, which is fighting a black box with a thesaurus. You cannot tell it "match the words this shopper typed and stop reaching," because that setting does not exist. The configuration ends right where you need it to begin.

How does configurable matching put you back in control?

It turns the one default into a choice. Instead of every query running through the same fixed relevance, you decide the mode: match words, match meaning, or both, and how far the meaning layer is allowed to stretch.

That is the difference between a black box and a dial. The dial lets you say "lean on exact terms for my catalog" or "let meaning fill the gaps," then change your mind if the results say you guessed wrong. Same search bar, but the relevance is a setting you own rather than a decision handed to you.

How do you control search mode in Sledge?

In Sledge Search you choose the matching mode for your store: keyword, semantic, or hybrid. Keyword matches the words. Semantic matches meaning. Hybrid runs both and blends them. Then you set the semantic strength, light, balanced, or strong, which decides how much meaning is allowed to override exact terms.

Run it through a real example. Set hybrid with light strength, and a search for a specific model number stays anchored to the words, while a vaguer query like "warm jacket for winter" still gets help from meaning. Set strength to strong, and the meaning layer reaches further, which suits a catalog where shoppers describe what they want more than they name it. Two layers sit underneath either way: typo tolerance catches misspellings, and synonyms you add or approve handle the vocabulary a model cannot infer.

Is semantic search just bad? Should I turn it off entirely?

The loud version of this complaint is "make it stop," and that is not quite the goal. Semantic matching is genuinely useful when a shopper describes a product instead of naming it. The problem was never that it exists, only that Shopify made it the only mode with no dial.

So the better move is not off, it is yours. Run keyword if your catalog is precise and shoppers search by exact terms. Run hybrid on light if you want the safety of words with a little help from meaning. Run strong if your shoppers describe more than they name. Sledge lets you set it, test it against your real queries, and change it, which is the control the forced change took away. Off is one option among several, not the only escape.

The search-control checklist

• List the queries that got worse after the relevance change, especially exact terms and model numbers.
• Choose a starting mode: keyword for precise catalogs, hybrid for mixed, semantic for descriptive shoppers.
• Set semantic strength to light, balanced, or strong and run your problem queries against it.
• Confirm typo tolerance is on so misspellings still resolve.
• Add synonyms for the vocabulary gaps the model cannot infer.
• Re-test the same queries after changes; keep the setting your results justify.

Related reading

Search · What is semantic search? · What is AI search? · How to find and fix zero-result searches on Shopify