A crash course on RAG systems—Part 9
...with implementation.
Part 9 of our RAG crash course is now available.
Read here: A Crash Course on Building RAG Systems – Part 9 (With Implementation).
What's inside Part 9?
We have covered a ton of useful details about RAG systems from Part 1 to Part 9:
Fundamentals of RAG
Evaluating RAG systems
Optimizing RAG systems.
Multimodal RAG (with foundations)
Graph RAG
Improving retrieval and rerankers with ColBERT
Now, it’s time to bring together all of that knowledge to build an extremely powerful RAG system that's powered by vision language models.
In Part 9, we are doing a deep architectural breakdown of ColPali, one of the SOTA in RAG systems right now, and how it offers a highly scalable and accurate solution for RAG.
The 26-minute deep dive will cover:
The motivation of ColPali with an intuitive real-life analogy
Why it is superior to traditional RAG
An implementation of ColPali and using it in the RAG system (completely beginner-friendly and with detailed instructions).
Integrating binary quantization into ColPali to make this system faster and applicable for use cases where low latency is paramount.
Read here: A Crash Course on Building RAG Systems – Part 9 (With Implementation).
What's in the crash course?
So far in this crash course series on building RAG systems, we’ve logically built on the foundations laid in the previous parts:
In Part 1, we explored the foundational components of RAG systems, the typical RAG workflow, and the tool stack, and also learned the implementation.
In Part 2, we understood how to evaluate RAG systems (with implementation).
In Part 3, we learned techniques to optimize RAG systems and handle millions/billions of vectors (with implementation).
In Part 4, we understood multimodality and covered techniques to build RAG systems on complex docs—ones that have images, tables, and texts (with implementation)
In Part 5, we understood the fundamental building blocks of multimodal RAG systems that will help us improve what we built in Part 4.
In Part 6, we utilized the learnings from Part 5 to build a much more extensive multimodal RAG system.
In Part 7, we learned how to build graph RAG systems. Here, we utilized a graph database to store information in the form of entities and relations, and build RAG apps over.
In Part 8, we learned how to improve retrieval and rerankers with ColBERT, to produce much more coherent responses at scale.
So, even if you are a complete beginner at RAG, it has you covered.
Why care about RAG?
RAG is a key NLP system that got massive attention due to one of the key challenges it solved around LLMs.
More specifically, if you know how to build a reliable RAG system, you can bypass the challenge and cost of fine-tuning LLMs.
That’s a considerable cost saving for enterprises.
And at the end of the day, all businesses care about impact. That’s it!
Can you reduce costs?
Drive revenue?
Can you scale ML models?
Predict trends before they happen?
Thus, the objective of this crash course is to help you implement reliable RAG systems, understand the underlying challenges, and develop expertise in building RAG apps on LLMs, which every industry cares about now.
Read the first part here →
Read the second part here →
Read the third part here →
Read the fourth part here →
Read the fifth part here [OPEN ACCESS] →
Read the sixth part here →
Read the seventh part here →
Read the eighth part here →
Read the ninth part here →
Of course, if you have never worked with LLMs, that’s okay. We cover everything in a practical and beginner-friendly way.
Thanks for reading!