Jeroen Overschie - The Levels of RAG 🦜

LLM's can be supercharged using a technique called RAG, allowing us to overcome dealbreaker problems like hallucinations or no access to internal data. RAG is gaining more industry momentum and is becoming rapidly more mature both in the open-source world and at major Cloud vendors. But what can we expect from RAG? What is the current state of the tech in the industry? What use-cases work well and which are more challenging? Let's find out together!
Retrieval Augmented Generation (RAG) is a popular technique to combine retrieval methods like vector search together with Large Language Models (LLM's). This gives us several advantages like retrieving extra information based on a user search query: allowing us to quote and cite LLM-generated answers. Because the underlying techniques are very broadly applicable, many types of data can be used to build up a RAG system, like textual data, tables, graphs or even images.
In this talk, we will deep dive into this popular emerging technique. Together, we will learn about: what the current state of RAG is, what tech is available to support you and what you expect to work well and what is still very challenging.
Join us if you 🫵:
Are interested in GenAI / LLM's and RAG
Want to know more about the current state of RAG
Would like to know when you can most successfully apply RAG
Contents of the talk 📌
[2 min] Intro
[3 min] Why RAG?
The case for RAG
The RAG advantage
… so how-to RAG?
[10 min] Basic RAG: Which ingredients make up a successful RAG system?
Data ingestion (OCR, PDF reading)
Chunking
Vector search
Keyword search
Mapping these components to the tech landscape
💥 Encountering difficulties
[10 min] Advanced RAG: Going multimodal
Tabular data
Graphs
Images
[4 min] Summing things up
The levels of RAG: from basic to advanced
GenAI community 🫂
Concluding remarks
[1 min] End
[30 minutes total]
❤️ Open Source Software
RAG and LLM’s are presented in a cloud-agnostic way. Many of the software libraries mentioned are open source. There is no agenda for representing any major cloud.