When an AI assistant searches your team's files, it almost never reads everything. It runs a search, gets back a handful of snippets, and answers from those. How good that search is determines whether the answer is grounded or guessed — and according to Cole Medin, who builds AI retrieval systems, the approach that holds up at scale is not one search method but two bolted together:

"The best strategy that I found is to combine keyword search and semantic search together. And they kind of cover each other's flaws, right? Like keyword search is able to find very specific wording or IDs, things like that. Then semantic search is able to find meanings, concepts that are related that don't actually have the same keywords."

The two methods fail in opposite ways, which is why combining them works. Keyword search is the familiar kind: it looks for literal matches. If your document says "ticket AUTH-4821" or "the Henderson contract," keyword search will find it every time. But ask it for documents about "reducing churn" and it will miss every file that discusses the same problem using the words "customer attrition" or "retention." It has no sense that those mean the same thing.

Semantic search is the inverse. It converts text into numbers that represent meaning, so it can connect your question to documents that are about the same concept even if they share no vocabulary with it. Ask about "reducing churn" and it will surface the attrition memo. But that strength is also its weakness: meaning is fuzzy, and when the thing you need is precise — a specific ID, an exact error code, a filename — semantic search can rank vaguely-related material above the exact match you wanted.

Hybrid search runs both and merges the results. Keyword catches the exact strings; semantic catches the related ideas. Each method covers the blind spot of the other, which is what Medin means by covering each other's flaws. In a database with thousands of documents, Slack threads, and code repositories, the practical effect is that the assistant gets a short, genuinely relevant list of snippets instead of a noisy pile of near-misses — and better snippets mean more accurate answers.

Who is this for? Honestly, mostly the people building these systems. This is an architectural decision, not a setting you toggle as an end user — it matters if you or your team are setting up an AI that searches a large internal knowledge base, or evaluating tools that claim to do so. If you are a non-developer who simply uses an assistant, you will not configure hybrid search yourself, but knowing the term is useful for one reason: it tells you what question to ask. When a vendor says their AI "searches your workspace," asking whether it does hybrid retrieval — or only semantic — is a concrete way to tell a serious implementation from a shallow one.

This is usable today, not a proposal. Medin describes it as a shipped, working strategy rather than an idea under discussion, and hybrid retrieval is standard practice in modern search infrastructure. The caveats a vendor would skip: combining two search systems means running and maintaining two search systems, which is more moving parts than either alone. And hybrid search improves what the assistant retrieves — it does not guarantee what the assistant does with it. A well-chosen snippet can still be misread.