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#47 Jorge Arango on Architecting Information for Search, Humans, and Artificial Intelligence | Search

#47 Jorge Arango on Architecting Information for Search, Humans, and Artificial Intelligence | Search

S2E30 · · 57:22

Today on How AI Is Built, Nicolay Gerold sits down with Jorge Arango, an expert in information architecture. Jorge emphasizes that aligning systems with users' mental models is more ...
#46 Marek Galovic on Building a Search Database From First Principles | S2 E29

#46 Marek Galovic on Building a Search Database From First Principles | S2 E29

S2E29 · · 53:29

Modern search is broken. There are too many pieces that are glued together.Vector databases for semantic searchText engines for keywordsRerankers to fix the resultsLLMs to understand...
#45 John Berryman on RAG As Two Things - Prompt Engineering and Search | Search

#45 John Berryman on RAG As Two Things - Prompt Engineering and Search | Search

S2E28 · · 01:02:44

John Berryman moved from aerospace engineering to search, then to ML and LLMs. His path: Eventbrite search → GitHub code search → data science → GitHub Copilot. He was drawn to more ...
#44 Semih Salihoglu on Graphs Aren't Just For Specialists Anymore | Search

#44 Semih Salihoglu on Graphs Aren't Just For Specialists Anymore | Search

S2E27 · · 01:03:35

Kuzu is an embedded graph database that implements Cypher as a library.It can be easily integrated into various environments—from scripts and Android apps to serverless platforms.Its...
#43 Juan Sequeda on Knowledge Graphs Won't Fix Bad Data | Search

#43 Juan Sequeda on Knowledge Graphs Won't Fix Bad Data | Search

S2E26 · · 01:10:59

Metadata is the foundation of any enterprise knowledge graph.By organizing both technical and business metadata, organizations create a “brain” that supports advanced applications li...
#42 Daniel Davis on Temporal RAG, Embracing Time for Smarter, Reliable Knowledge Graphs | Search

#42 Daniel Davis on Temporal RAG, Embracing Time for Smarter, Reliable Knowledge Graphs | Search

S2E25 · · 01:33:44

Daniel Davis is an expert on knowledge graphs. He has a background in risk assessment and complex systems—from aerospace to cybersecurity. Now he is working on “Temporal RAG” in Trus...
#41 Robert Caulk on Context Engineering, How Knowledge Graphs Help LLMs Reason | Search

#41 Robert Caulk on Context Engineering, How Knowledge Graphs Help LLMs Reason | Search

S2E24 · · 01:33:35

Robert Caulk runs Emergent Methods, a research lab building news knowledge graphs. With a Ph.D. in computational mechanics, he spent 12 years creating open-source tools for machine l...
#40 Zain Hasan on Vector Database Quantization, Product, Binary, and Scalar | Search (repost)

#40 Zain Hasan on Vector Database Quantization, Product, Binary, and Scalar | Search (repost)

S2E23 · · 52:12

When you store vectors, each number takes up 32 bits.With 1000 numbers per vector and millions of vectors, costs explode.A simple chatbot can cost thousands per month just to store a...
#39 Alex Garcia on Local-First Search, How to Push Search To End-Devices | Search

#39 Alex Garcia on Local-First Search, How to Push Search To End-Devices | Search

S2E22 · · 53:09

Alex Garcia is a developer focused on making vector search accessible and practical. As he puts it: "I'm a SQLite guy. I use SQLite for a lot of projects... I want an easier vector s...
#38 Trey Grainger on AI-Powered Search, Context Is King, But Your RAG System Ignores Two-Thirds of It | Search

#38 Trey Grainger on AI-Powered Search, Context Is King, But Your RAG System Ignores Two-Thirds of It | Search

S2E21 · · 01:14:24

Today, I (Nicolay Gerold) sit down with Trey Grainger, author of the book AI-Powered Search. We discuss the different techniques for search and recommendations and how to combine the...
#37 Brandon Smith on Chunking for RAG: Stop Breaking Your Documents Into Meaningless Pieces | Search

#37 Brandon Smith on Chunking for RAG: Stop Breaking Your Documents Into Meaningless Pieces | Search

S2E20 · · 49:13

Today we are back continuing our series on search. We are talking to Brandon Smith, about his work for Chroma. He led one of the largest studies in the field on different chunking te...
#36 Adiren Morisot on How AI Can Start Teaching Itself - Synthetic Data Deep Dive | Search

#36 Adiren Morisot on How AI Can Start Teaching Itself - Synthetic Data Deep Dive | Search

S2E19 · · 48:11

Most LLMs you use today already use synthetic data.It’s not a thing of the future.The large labs use a large model (e.g. gpt-4o) to generate training data for a smaller one (gpt-4o-m...
#35 Stephen Batifol on A Search System That Learns As You Use It (Agentic RAG) | Search

#35 Stephen Batifol on A Search System That Learns As You Use It (Agentic RAG) | Search

S2E18 · · 45:30

Modern RAG systems build on flexibility.At their core, they match each query with the best tool for the job.They know which tool fits each task. When you ask about sales numbers, the...
#34 Philippe Noel on Rethinking Search Inside Postgres, From Lexemes to BM25 | Search

#34 Philippe Noel on Rethinking Search Inside Postgres, From Lexemes to BM25 | Search

S2E17 · · 47:16

Many companies use Elastic or OpenSearch and use 10% of the capacity.They have to build ETL pipelines.Get data Normalized.Worry about race conditions.All in all. At the moment, when ...
#33 Saahil Ognawala on RAG's Biggest Problems & How to Fix It (ft. Synthetic Data) | Search

#33 Saahil Ognawala on RAG's Biggest Problems & How to Fix It (ft. Synthetic Data) | Search

S2E16 · · 51:26

RAG isn't a magic fix for search problems. While it works well at first, most teams find it's not good enough for production out of the box. The key is to make it better step by step...
#32 Max Buckley on Improving Documentation Quality for RAG Systems | Search

#32 Max Buckley on Improving Documentation Quality for RAG Systems | Search

S2E15 · · 46:37

Documentation quality is the silent killer of RAG systems. A single ambiguous sentence might corrupt an entire set of responses. But the hardest part isn't fixing errors - it's findi...
#31 David Tippet on BM25 As The Workhorse Of Search; Vectors Are Its Visionary Cousin | Search

#31 David Tippet on BM25 As The Workhorse Of Search; Vectors Are Its Visionary Cousin | Search

S2E14 · · 54:05

Ever wondered why vector search isn't always the best path for information retrieval?Join us as we dive deep into BM25 and its unmatched efficiency in our latest podcast episode with...
#30 Charles Xie on Vector Search at Scale, Why One Size Doesn't Fit All | Search

#30 Charles Xie on Vector Search at Scale, Why One Size Doesn't Fit All | Search

S2E13 · · 36:26

Ever wondered why your vector search becomes painfully slow after scaling past a million vectors? You're not alone - even tech giants struggle with this.Charles Xie, founder of Zilli...
#29 Russ and Stuart Cam on Search Systems at Scale, Avoiding Local Maxima and Other Engineering Lessons | Search

#29 Russ and Stuart Cam on Search Systems at Scale, Avoiding Local Maxima and Other Engineering Lessons | Search

S2E12 · · 54:47

Modern search systems face a complex balancing act between performance, relevancy, and cost, requiring careful architectural decisions at each layer.While vector search generates buz...
#28 Michael Gunther on Training Multi-Modal AI, Inside the Jina CLIP Embedding Model | Search

#28 Michael Gunther on Training Multi-Modal AI, Inside the Jina CLIP Embedding Model | Search

S2E11 · · 49:22

Today we are talking to Michael Günther, a senior machine learning scientist at Jina about his work on JINA Clip.Some key points:Uni-modal embeddings convert a single type of input (...
#27 Chang She on Building the database for AI, Multi-modal AI, Multi-modal Storage | Search (repost)

#27 Chang She on Building the database for AI, Multi-modal AI, Multi-modal Storage | Search (repost)

S2E10 · · 44:54

Imagine a world where data bottlenecks, slow data loaders, or memory issues on the VM don't hold back machine learning.Machine learning and AI success depends on the speed you can it...
#26 Mor Kapronczay on Embedding Numbers, Categories, Locations, Images, Text, and The World | Search

#26 Mor Kapronczay on Embedding Numbers, Categories, Locations, Images, Text, and The World | Search

S2E9 · · 46:44

Today’s guest is Mór Kapronczay. Mór is the Head of ML at superlinked. Superlinked is a compute framework for your information retrieval and feature engineering systems, where they t...
#25 Jessica Talisman on Data Models to Remove Ambiguity from AI and Search | Search

#25 Jessica Talisman on Data Models to Remove Ambiguity from AI and Search | Search

S2E8 · · 58:40

Today we have Jessica Talisman with us, who is working as an Information Architect at Adobe. She is (in my opinion) the expert on taxonomies and ontologies.That’s what you will learn...
#24 Jo Bergum on How ColPali is Changing Information Retrieval | Search

#24 Jo Bergum on How ColPali is Changing Information Retrieval | Search

S2E7 · · 54:57

ColPali makes us rethink how we approach document processing.ColPali revolutionizes visual document search by combining late interaction scoring with visual language models. This app...
#23 Aamir Shakir on The Power of Rerankers in Modern Search | Search

#23 Aamir Shakir on The Power of Rerankers in Modern Search | Search

S2E6 · · 42:29

Today, we're talking to Aamir Shakir, the founder and baker at mixedbread.ai, where he's building some of the best embedding and re-ranking models out there. We go into the world of ...
#22 Nils Reimers on the Limits of Embeddings, Out-of-Domain Data, Long Context, Finetuning (and How We're Fixing It) | Search

#22 Nils Reimers on the Limits of Embeddings, Out-of-Domain Data, Long Context, Finetuning (and How We're Fixing It) | Search

S2E5 · · 46:06

Text embeddings have limitations when it comes to handling long documents and out-of-domain data.Today, we are talking to Nils Reimers. He is one of the researchers who kickstarted t...
#21 Nirant Kaasliwal on The Problems You Will Encounter With RAG At Scale And How To Prevent (or fix) Them | Search

#21 Nirant Kaasliwal on The Problems You Will Encounter With RAG At Scale And How To Prevent (or fix) Them | Search

S2E4 · · 50:09

Hey! Welcome back.Today we look at how we can get our RAG system ready for scale.We discuss common problems and their solutions, when you introduce more users and more requests to yo...
#20 Doug Turnbull on The Evolution of Search, Finding Search Signals, GenAI Augmented Retrieval | Search

#20 Doug Turnbull on The Evolution of Search, Finding Search Signals, GenAI Augmented Retrieval | Search

S2E3 · · 52:16

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, i...
#19 Charlie Hull on Data-driven Search Optimization, Analysing Relevance | Search

#19 Charlie Hull on Data-driven Search Optimization, Analysing Relevance | Search

S2E2 · · 51:14

In this episode, we talk data-driven search optimizations with Charlie Hull.Charlie is a search expert from Open Source Connections. He has built Flax, one of the leading open source...
#18 Daniel Tunkelang on Query Understanding: Doing The Work Before The Query Hits The Database | Search

#18 Daniel Tunkelang on Query Understanding: Doing The Work Before The Query Hits The Database | Search

S2E1 · · 53:02

Welcome back to How AI Is Built. We have got a very special episode to kick off season two. Daniel Tunkelang is a search consultant currently working with Algolia. He is a leader in ...