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#047 Architecting Information for Search, Humans, and Artificial Intelligence

#047 Architecting Information for Search, Humans, and Artificial Intelligence

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 ...
#046 Building a Search Database From First Principles

#046 Building a Search Database From First Principles

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...
#045 RAG As Two Things - Prompt Engineering and Search

#045 RAG As Two Things - Prompt Engineering and 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 ...
#044 Graphs Aren't Just For Specialists Anymore

#044 Graphs Aren't Just For Specialists Anymore

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...
#043 Knowledge Graphs Won't Fix Bad Data

#043 Knowledge Graphs Won't Fix Bad Data

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...
#042 Temporal RAG, Embracing Time for Smarter, Reliable Knowledge Graphs

#042 Temporal RAG, Embracing Time for Smarter, Reliable Knowledge Graphs

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...
#041 Context Engineering, How Knowledge Graphs Help LLMs Reason

#041 Context Engineering, How Knowledge Graphs Help LLMs Reason

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...
#040 Vector Database Quantization, Product, Binary, and Scalar

#040 Vector Database Quantization, Product, Binary, and Scalar

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...
#039 Local-First Search, How to Push Search To End-Devices

#039 Local-First Search, How to Push Search To End-Devices

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...
#038 AI-Powered Search, Context Is King, But Your RAG System Ignores Two-Thirds of It

#038 AI-Powered Search, Context Is King, But Your RAG System Ignores Two-Thirds of It

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...
#037 Chunking for RAG: Stop Breaking Your Documents Into Meaningless Pieces

#037 Chunking for RAG: Stop Breaking Your Documents Into Meaningless Pieces

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...
#036 How AI Can Start Teaching Itself - Synthetic Data Deep Dive

#036 How AI Can Start Teaching Itself - Synthetic Data Deep Dive

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...
#035 A Search System That Learns As You Use It (Agentic RAG)

#035 A Search System That Learns As You Use It (Agentic RAG)

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...
#034 Rethinking Search Inside Postgres, From Lexemes to BM25

#034 Rethinking Search Inside Postgres, From Lexemes to BM25

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 ...
#033 RAG's Biggest Problems & How to Fix It (ft. Synthetic Data)

#033 RAG's Biggest Problems & How to Fix It (ft. Synthetic Data)

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...
#032 Improving Documentation Quality for RAG Systems

#032 Improving Documentation Quality for RAG Systems

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...
#031 BM25 As The Workhorse Of Search; Vectors Are Its Visionary Cousin

#031 BM25 As The Workhorse Of Search; Vectors Are Its Visionary Cousin

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...
#030 Vector Search at Scale, Why One Size Doesn't Fit All

#030 Vector Search at Scale, Why One Size Doesn't Fit All

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...
#029 Search Systems at Scale, Avoiding Local Maxima and Other Engineering Lessons

#029 Search Systems at Scale, Avoiding Local Maxima and Other Engineering Lessons

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...
#028 Training Multi-Modal AI, Inside the Jina CLIP Embedding Model

#028 Training Multi-Modal AI, Inside the Jina CLIP Embedding Model

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 (...
#027 Building the database for AI, Multi-modal AI, Multi-modal Storage

#027 Building the database for AI, Multi-modal AI, Multi-modal Storage

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...
#026 Embedding Numbers, Categories, Locations, Images, Text, and The World

#026 Embedding Numbers, Categories, Locations, Images, Text, and The World

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...
#025 Data Models to Remove Ambiguity from AI and Search

#025 Data Models to Remove Ambiguity from AI and 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...
#024 How ColPali is Changing Information Retrieval

#024 How ColPali is Changing Information Retrieval

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...
#023 The Power of Rerankers in Modern Search

#023 The Power of Rerankers in Modern 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 ...
#022 The Limits of Embeddings, Out-of-Domain Data, Long Context, Finetuning (and How We're Fixing It)

#022 The Limits of Embeddings, Out-of-Domain Data, Long Context, Finetuning (and How We're Fixing It)

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...
#021 The Problems You Will Encounter With RAG At Scale And How To Prevent (or fix) Them

#021 The Problems You Will Encounter With RAG At Scale And How To Prevent (or fix) Them

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...
#020 The Evolution of Search, Finding Search Signals, GenAI Augmented Retrieval

#020 The Evolution of Search, Finding Search Signals, GenAI Augmented Retrieval

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...
#019 Data-driven Search Optimization, Analysing Relevance

#019 Data-driven Search Optimization, Analysing Relevance

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...
#018 Query Understanding: Doing The Work Before The Query Hits The Database

#018 Query Understanding: Doing The Work Before The Query Hits The Database

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 ...