Dense Vector Retrieval
Hosted by Raul Salles de Padua and Can Temizyurek
Tue, May 12, 2026
11:00 PM UTC (30 minutes)
Virtual (Zoom)
Free to join
Go deeper with a course
LLM Ops - Large Language Models in Production
Dr. Greg Loughnane and Chris "The LLM Wizard 🪄" Alexiuk
Founder & CEO @ AI Makerspace. Co-Founder & CTO @ AI Makerspace
Tue, May 12, 2026
11:00 PM UTC (30 minutes)
Virtual (Zoom)
Free to join
Go deeper with a course
LLM Ops - Large Language Models in Production
Dr. Greg Loughnane and Chris "The LLM Wizard 🪄" Alexiuk
Founder & CEO @ AI Makerspace. Co-Founder & CTO @ AI Makerspace
What you'll learn
Introduction to classic RAG
Meet context engineering through the lense of retrieval, RAG, and the LLM application stack
Overview of embeddings and similarity search
Understand the role that different types of LLMs play in a RAG process
Build a RAG application from scratch in Python
Without using any off-the-shelf tools, build a truly Pythonic RAG application
Why this topic matters
To understand context engineering, you must first understand in-context learning. The easiest way to approach this concept for software engineers who are new to data science is through the lens of RAG.
Once you understand the process of RAG, then understanding the historical context of LLMs (including chat and embedding models) is a much simpler task.
You'll learn from
Raul Salles de Padua
Principal AI & ML Engineer @ Rumble
Lead instructor for AI Makerspace's The AI Engineering Certification v1.0. Cohort starts June 2nd!
Can Temizyurek
Maintainer, Evalite (>200k downloads/wk)
Code instructor for AI Makerspace's The AI Engineering Certification v1.0. Cohort starts June 2nd!