I share search tips, blog articles, and free events I'm hosting about the search+retreval industry, vector databases, information retrieval and more.
Content understanding IS Query understanding (daily search tip)
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A user searches for But what can you do with it? You could literally memorize these amazing results and show them to future users. Maybe that’s the right thing to do. But we can take it up a notch. Now imagine those products have attributes. Like color. We observe: eighty percent of Now you’ve understood something about Can you use it? You can use it IF you trust this product color classification. That’s why content understanding IS query understanding:
That’s the START of your query understanding journey. After that you can get fancier. But it always starts with categorizing your content first. -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.
Talks this week + other events. Hope you can make it and help keep this community awesome 😎 First - Leonie Monigatti will share how Context Engineering IS Agentic Search. Tuesday, 10:30AM ET What everyone is missing: when people talk aobut "context engineering", they really ought to be improving search. Leonie will cast aside myths about context engineering to show how agents build their own context via retrieval. And THAT, not prompting magic, decides whether your AI app is successful....
Late interaction is having a moment. The team at LightOn - including superstar developer Antoine Chaffin - has demonstrated how a 150M(!) late interaction model beats much larger models - some up to 8B parameters. David beats Goliath! Better search only cost you less! Tested on what dataset? BrowseComp. BrowseComp asks difficult questions requiring detailed, complex research. Tasks you can imagine agents chugging away, searching, getting frustrated and lost. Here’s an example prompt / answer...
How do teams choose vector databases / search engines? People wrack their brains between Elasticsearch/OpenSearch/Solr/Vespa/Pinecone/Turbopuffer/Weaviate/…? First things first - DO NOT start with a feature matrix. Start with the simple question: What is my team most comfortable with? That’s the default. If everyone can go deep in one system, don’t overcomplicate the decision. It might be good enough to stop here. NEXT - consider the high-level characteristics of the project. Use these as...