How AI Is Built

Mar 06 2025 49 ep. 45 mins 23
How AI Is Built Podcast artwork

Real engineers. Real deployments. Zero hype. We interview the top engineers who actually put AI in production. Learn what the best engineers have figured out through years of experience. Hosted by Nicolay Gerold, CEO of Aisbach and CTO at Proxdeal and Multiply Content.

  Technology
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RAG is two things. Prompt Engineering and Search. Keep it Separate | S2 E28

Mar 06 2025 62 mins  
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 math and ML throughout his career.RAG Explained"RAG is not a thing. RAG is two things." It breaks into:Search - finding relevant informationPrompt engineering - presenting that information to the

Graphs aren't just for specialists anymore. They are one import away | S2 E27

Feb 28 2025 63 mins  
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 design supports both ephemeral, in-memory graphs (ideal for temporary computations) and large-scale persistent graphs where traditional systems struggle with performance and scalability.Key Architect

Knowledge Graphs Won't Fix Bad Data | S2 E26

Feb 20 2025 70 mins  
Metadata is the foundation of any enterprise knowledge graph.By organizing both technical and business metadata, organizations create a “brain” that supports advanced applications like AI-driven data assistants.The goal is to achieve economies of scale—making data reusable, traceable, and ultimately more valuable.Juan Sequeda is a leading expert in enterprise knowledge graphs

Temporal RAG: Embracing Time for Smarter, Reliable Knowledge Graphs | S2 E25

Feb 13 2025 93 mins  
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 TrustGraph.Time is a critical—but often ignored—dimension in data. Whether it’s threat intelligence, legal contracts, or API documentation, every data point has a temporal context that affects i

Context is King: How Knowledge Graphs Help LLMs Reason

Feb 06 2025 93 mins  
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 learning and data analysis. His work on projects like Flowdapt (model serving) and FreqAI (adaptive modeling) has earned over 1,000 academic citations.His team built AskNews, which he calls "the largest

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

Jan 31 2025 52 mins  
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 and search through vectors.The Fix: QuantizationThink of it like image compression. JPEGs look almost as good as raw photos but take up far less space. Quantization does the same for vectors.Today we are

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

Jan 23 2025 53 mins  
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 search thing that I don't have to install 10,000 dependencies to use.”Core Mantra: "Simple, Local, Scalable"Why SQLite Vec?"I didn't go along thinking, 'Oh, I want to build vector search, let me find a

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

Jan 09 2025 74 mins  
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 them.While RAG (Retrieval-Augmented Generation) has become a buzzword in AI, Trey argues that the current understanding of "RAG" is overly simplified – it's actually a bidirectional process he calls "GAR

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

Jan 03 2025 49 mins  
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 techniques. So today we will look at how we can unfuck our RAG systems from badly chosen chunking hyperparameters.The biggest lie in RAG is that semantic search is simple. The reality is that it's easy to

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

Dec 19 2024 48 mins  
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-mini).This lets you build fast, cheap models that do one thing well.This is “distillation”.But the vision for synthetic data is much bigger.Enable people to train specialized AI systems without hav