Relevant retrieval w/ predictable latency (daily search tip)

Published about 1 month ago • 1 min read

The higher the scale, the stronger the incentive to simplify your retrieval.

There’s two conflicting incentives:

Improving relevance: Requiring more complex retrieval to get all the best candidates
Improving reliability: Consistent latency and throughput + easier for an infra engineer to manage / debug

What does “simpler retrieval” look like?

Single vector retrieval with a few filters
A first pass BM25 retrieval with a recency boost
An assumption you’re fetching top 1000 and reranking outside the search engine

Of course, how far you sacrifice relevance for reliability requires measurement. And that requires actually deploying your retrieval changes early. Then measuring under actual load using shadow traffic.

Liberating Search from the Search Engine

-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:

Vector search means query understanding!? (daily search tip)

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,...

about 19 hours ago • 1 min read

Use RRF sparingly in hybrid search

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...

3 days ago • 1 min read

Blog post on Bayesian BM25

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:

4 days ago • 1 min read