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1 Ep 563: ChatGPT's New Custom GPT's: Advanced techniques to win back time 46:43
#020 The Evolution of Search, Finding Search Signals, GenAI Augmented Retrieval
Manage episode 438354708 series 3585930
In this episode of How AI is Built, Nicolay Gerold interviews Doug Turnbull, a search engineer at Reddit and author on “Relevant Search”. They discuss how methods and technologies, including large language models (LLMs) and semantic search, contribute to relevant search results.
Key Highlights:
- Defining relevance is challenging and depends heavily on user intent and context
- Combining multiple search techniques (keyword, semantic, etc.) in tiers can improve results
- LLMs are emerging as a powerful tool for augmenting traditional search approaches
- Operational concerns often drive architectural decisions in large-scale search systems
- Underappreciated techniques like LambdaMART may see a resurgence
Key Quotes:
"There's not like a perfect measure or definition of what a relevant search result is for a given application. There are a lot of really good proxies, and a lot of really good like things, but you can't just like blindly follow the one objective, if you want to build a good search product." - Doug Turnbull
"I think 10 years ago, what people would do is they would just put everything in Solr, Elasticsearch or whatever, and they would make the query to Elasticsearch pretty complicated to rank what they wanted... What I see people doing more and more these days is that they'll use each retrieval source as like an independent piece of infrastructure." - Doug Turnbull on the evolution of search architecture
"Honestly, I feel like that's a very practical and underappreciated thing. People talk about RAG and I talk, I call this GAR - generative AI augmented retrieval, so you're making search smarter with generative AI." - Doug Turnbull on using LLMs to enhance search
"LambdaMART and gradient boosted decision trees are really powerful, especially for when you're expressing your re-ranking as some kind of structured learning problem... I feel like we'll see that and like you're seeing papers now where people are like finding new ways of making BM25 better." - Doug Turnbull on underappreciated techniques
Doug Turnbull
Nicolay Gerold:
Chapters
00:00 Introduction and Guest Introduction 00:52 Understanding Relevant Search Results 01:18 Search Behavior on Social Media 02:14 Challenges in Defining Relevance 05:12 Query Understanding and Ranking Signals 10:57 Evolution of Search Technologies 15:15 Combining Search Techniques 21:49 Leveraging LLMs and Embeddings 25:49 Operational Considerations in Search Systems 39:09 Concluding Thoughts and Future Directions
59 episodes
Manage episode 438354708 series 3585930
In this episode of How AI is Built, Nicolay Gerold interviews Doug Turnbull, a search engineer at Reddit and author on “Relevant Search”. They discuss how methods and technologies, including large language models (LLMs) and semantic search, contribute to relevant search results.
Key Highlights:
- Defining relevance is challenging and depends heavily on user intent and context
- Combining multiple search techniques (keyword, semantic, etc.) in tiers can improve results
- LLMs are emerging as a powerful tool for augmenting traditional search approaches
- Operational concerns often drive architectural decisions in large-scale search systems
- Underappreciated techniques like LambdaMART may see a resurgence
Key Quotes:
"There's not like a perfect measure or definition of what a relevant search result is for a given application. There are a lot of really good proxies, and a lot of really good like things, but you can't just like blindly follow the one objective, if you want to build a good search product." - Doug Turnbull
"I think 10 years ago, what people would do is they would just put everything in Solr, Elasticsearch or whatever, and they would make the query to Elasticsearch pretty complicated to rank what they wanted... What I see people doing more and more these days is that they'll use each retrieval source as like an independent piece of infrastructure." - Doug Turnbull on the evolution of search architecture
"Honestly, I feel like that's a very practical and underappreciated thing. People talk about RAG and I talk, I call this GAR - generative AI augmented retrieval, so you're making search smarter with generative AI." - Doug Turnbull on using LLMs to enhance search
"LambdaMART and gradient boosted decision trees are really powerful, especially for when you're expressing your re-ranking as some kind of structured learning problem... I feel like we'll see that and like you're seeing papers now where people are like finding new ways of making BM25 better." - Doug Turnbull on underappreciated techniques
Doug Turnbull
Nicolay Gerold:
Chapters
00:00 Introduction and Guest Introduction 00:52 Understanding Relevant Search Results 01:18 Search Behavior on Social Media 02:14 Challenges in Defining Relevance 05:12 Query Understanding and Ranking Signals 10:57 Evolution of Search Technologies 15:15 Combining Search Techniques 21:49 Leveraging LLMs and Embeddings 25:49 Operational Considerations in Search Systems 39:09 Concluding Thoughts and Future Directions
59 episodes
All episodes
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1 #052 Don't Build Models, Build Systems That Build Models 59:22

1 #051 Build systems that can be debugged at 4am by tired humans with no context 1:05:51

1 #050 Bringing LLMs to Production: Delete Frameworks, Avoid Finetuning, Ship Faster 1:06:57

1 #050 TAKEAWAYS Bringing LLMs to Production: Delete Frameworks, Avoid Finetuning, Ship Faster 11:00

1 #049 BAML: The Programming Language That Turns LLMs into Predictable Functions 1:02:38

1 #049 TAKEAWAYS BAML: The Programming Language That Turns LLMs into Predictable Functions 1:12:34

1 #048 TAKEAWAYS Why Your AI Agents Need Permission to Act, Not Just Read 7:06

1 #048 Why Your AI Agents Need Permission to Act, Not Just Read 57:02

1 #047 Architecting Information for Search, Humans, and Artificial Intelligence 57:21

1 #046 Building a Search Database From First Principles 53:28

1 #045 RAG As Two Things - Prompt Engineering and Search 1:02:43

1 #044 Graphs Aren't Just For Specialists Anymore 1:03:34

1 #043 Knowledge Graphs Won't Fix Bad Data 1:10:58

1 #042 Temporal RAG, Embracing Time for Smarter, Reliable Knowledge Graphs 1:33:43

1 #041 Context Engineering, How Knowledge Graphs Help LLMs Reason 1:33:34
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