I share search tips, blog articles, and free events I'm hosting about the search+retreval industry, vector databases, information retrieval and more.
Underrated: retention; overrated: conversions (daily search tip)
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Have you been to a conversion-crazy site? It’s nuts. Their site screams at you. They probably have the modern version of the HTML blink tag. Popups everywhere just won't go away. Buy buy buy! It’s fun to go to a physical store when you can browse the shelves, talk to customer service, and get help. People avoid stores lacking information and only high pressure salespeople in your face. If your search stinks of pressure, users will retreat. They’ll stay on Google. They win precisely because they don’t push you. Before obsessing over conversion:
User acquisition and retention can be a blindspot for search teams. Make sure its not for your team! -Doug Events · Consulting · Training (use code search-tips) You're subscribed to Doug Turnbull's daily search tips where I share tips, blog articles, events, and more. You can always manage your profile: |
Doug Turnbull
I share search tips, blog articles, and free events I'm hosting about the search+retreval industry, vector databases, information retrieval and more.
Good vector search means more than embeddings. Embeddings don’t know when a result matches / doesn’t match. Similarity floors don’t work consistently - a cutoff that works for one query might be disastrous for another. Even worse: your embedding usually can’t capture every little bit of meaning from your corpus. You need to efficiently pick the best top N candidates from your vector database. What do you need? Query Understanding - translating the query to domain language (categories, colors,...
Reciprocal Rank Fusion merges one system’s search ranking with another’s (ie lexical + embedding search). RRF scores a document with ∑1/rank of each underlying system. I’ve found RRF is not enough. Here’s the typical pattern I see on teams: A mature lexical solution exists. It’s pretty good, The team wants to add untuned, embedding based retrieval, They deploy a vector DB, and RRF embedding results with the mature system, Disaster ensues! The poor embedding results drag down the lexical...
Just sharing my post on Bayesian BM25 and other ways of normalizing BM25 scores. Enjoy! https://softwaredoug.com/blog/2026/03/06/probabilistic-bm25-utopia Do you have any thoughts on normalizing BM25 scores? -Doug Events · Consulting · Training (use code search-tips) You're subscribed to Doug Turnbull's daily search tips where I share tips, blog articles, events, and more. You can always manage your profile: