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Context is King: How Knowledge Graphs Help LLMs Reason

Context is King: 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...
Inside Vector Database Quantization: Product, Binary, and Scalar | S2 E23

Inside Vector Database Quantization: Product, Binary, and Scalar | S2 E23

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...
Local-First Search: How to Push Search To End-Devices | S2 E22

Local-First Search: How to Push Search To End-Devices | S2 E22

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...
AI-Powered Search: Context Is King, But Your RAG System Ignores Two-Thirds of It | S2 E21

AI-Powered Search: Context Is King, But Your RAG System Ignores Two-Thirds of It | S2 E21

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...
Chunking for RAG: Stop Breaking Your Documents Into Meaningless Pieces | S2 E20

Chunking for RAG: Stop Breaking Your Documents Into Meaningless Pieces | S2 E20

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...
How AI Can Start Teaching Itself - Synthetic Data Deep Dive | S2 E18

How AI Can Start Teaching Itself - Synthetic Data Deep Dive | S2 E18

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...
A Search System That Learns As You Use It (Agentic RAG) | S2 E18

A Search System That Learns As You Use It (Agentic RAG) | S2 E18

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...
Rethinking Search Inside Postgres, From Lexemes to BM25 | S2 E17

Rethinking Search Inside Postgres, From Lexemes to BM25 | S2 E17

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 ...
RAG's Biggest Problems & How to Fix It (ft. Synthetic Data) | S2 E16

RAG's Biggest Problems & How to Fix It (ft. Synthetic Data) | S2 E16

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...
From Ambiguous to AI-Ready: Improving Documentation Quality for RAG Systems | S2 E15

From Ambiguous to AI-Ready: Improving Documentation Quality for RAG Systems | S2 E15

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...
BM25 is the workhorse of search; vectors are its visionary cousin | S2 E14

BM25 is the workhorse of search; vectors are its visionary cousin | S2 E14

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...
Vector Search at Scale: Why One Size Doesn't Fit All | S2 E13

Vector Search at Scale: Why One Size Doesn't Fit All | S2 E13

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...
Search Systems at Scale: Avoiding Local Maxima and Other Engineering Lessons | S2 E12

Search Systems at Scale: Avoiding Local Maxima and Other Engineering Lessons | S2 E12

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...
Training Multi-Modal AI: Inside the Jina CLIP Embedding Model | S2 E11

Training Multi-Modal AI: Inside the Jina CLIP Embedding Model | S2 E11

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 (...
Building the database for AI, Multi-modal AI, Multi-modal Storage | S2 E10

Building the database for AI, Multi-modal AI, Multi-modal Storage | S2 E10

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...
Numbers, categories, locations, images, text. How to embed the world? | S2 E9

Numbers, categories, locations, images, text. How to embed the world? | S2 E9

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...
Building Taxonomies: Data Models to Remove Ambiguity from AI and Search | S2 E8

Building Taxonomies: Data Models to Remove Ambiguity from AI and Search | S2 E8

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...
From PDFs to Pixels: How ColPali is Changing Information Retrieval | S2 E7

From PDFs to Pixels: How ColPali is Changing Information Retrieval | S2 E7

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...
Beyond Embeddings: The Power of Rerankers in Modern Search | S2 E6

Beyond Embeddings: The Power of Rerankers in Modern Search | S2 E6

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 ...
Limits of Embeddings: Out-of-Domain Data, Long Context, Finetuning (and How We're Fixing It) | S2 E5

Limits of Embeddings: Out-of-Domain Data, Long Context, Finetuning (and How We're Fixing It) | S2 E5

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...
RAG at Scale: The problems you will encounter and how to prevent (or fix) them | S2 E4

RAG at Scale: The problems you will encounter and how to prevent (or fix) them | S2 E4

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...
From Keywords to AI (to GAR): The Evolution of Search, Finding Search Signals | S2 E3

From Keywords to AI (to GAR): The Evolution of Search, Finding Search Signals | S2 E3

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...
Data-driven Search Optimization, Analysing Relevance | S2 E2

Data-driven Search Optimization, Analysing Relevance | S2 E2

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...
Query Understanding: Doing The Work Before The Query Hits The Database | S2 E1

Query Understanding: Doing The Work Before The Query Hits The Database | S2 E1

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 ...
Season 2 Trailer: Mastering Search

Season 2 Trailer: Mastering Search

S2Trailer · · 04:16

Today we are launching the season 2 of How AI Is Built.The last few weeks, we spoke to a lot of regular listeners and past guests and collected feedback. Analyzed our episode data. A...
Unlocking Value from Unstructured Data, Real-World Applications of Generative AI | ep 17

Unlocking Value from Unstructured Data, Real-World Applications of Generative AI | ep 17

S1E17 · · 36:28

In this episode of "How AI is Built," host Nicolay Gerold interviews Jonathan Yarkoni, founder of Reach Latent. Jonathan shares his expertise in extracting value from unstructured da...
Data Processing for AI, Integrating AI into Data Pipelines, Spark | ep 16

Data Processing for AI, Integrating AI into Data Pipelines, Spark | ep 16

S1E16 · · 46:26

This episode of "How AI Is Built" is all about data processing for AI. Abhishek Choudhary and Nicolay discuss Spark and alternatives to process data so it is AI-ready.Spark is a dist...
Building AI Agents for the Enterprise: Realistic Use Cases, Cost Controls, Seamless UX | ep 15

Building AI Agents for the Enterprise: Realistic Use Cases, Cost Controls, Seamless UX | ep 15

S1E15 · · 35:12

In this episode, Nicolay talks with Rahul Parundekar, founder of AI Hero, about the current state and future of AI agents. Drawing from over a decade of experience working on agent t...
Building Predictable Agents: Prompting, Compression, and Memory Strategies | ep 14

Building Predictable Agents: Prompting, Compression, and Memory Strategies | ep 14

S1E14 · · 32:14

In this conversation, Nicolay and Richmond Alake discuss various topics related to building AI agents and using MongoDB in the AI space. They cover the use of agents and multi-agents...
Data Integration and Ingestion for AI & LLMs, Architecting Data Flows | changelog 3

Data Integration and Ingestion for AI & LLMs, Architecting Data Flows | changelog 3

S1E14 · · 14:53

In this episode, Kirk Marple, CEO and founder of Graphlit, shares his expertise on building efficient data integrations. Kirk breaks down his approach using relatable concepts: The...