RAG is an abbreviation of Retrieval Augmented Generation. Let’s breakdown this term to get a clear overview of what RAG is:
R -> Retrieval
A -> Augmented
G -> Generation
So basically, the LLM that we use today is not up to the date. If I ask a question to a LLM let’s say ChatGPT, it may be hallucinated and give us the incorrect answer. To overcome this situation, we train our LLM with some more data(data which is only accessible to limited people, not globally). Then we ask some questions to the LLM trained on that data. Surely, it will give us the relevant information. Here are the some situation that may occur if we don’t use RAG:
You can have a look at the diagram mentioned below:
RAG is a hybrid system which combines the strength of a retrieval based system with LLMs to generate more accurate, relevant and informed decisions. This method leverages external knowledge sources during the generation process, enhancing the model’s ability to provide up-to-date and contextually appropriate information. In the above diagram:
Now I know you are fully interested in learning RAG from basic to advanced. Now let me tell you the perfect roadmap to learn RAG in just 5 days. Yes, you heard it right, in just 5 days you can learn the RAG system. Let’s dive straight into the roadmap:
The core objective of day 1 is understanding the RAG at a high level and exploring what are the key components of RAG. Below are the breakdown of the topics for day 1
Overview of RAG:
Key Components:
The core objective of day 2 is to Successfully implement a retrieval system (even a basic one).Below are the breakdown of the topics for day 2
Deep Dive into Retrieval Models:
Implementation of Retrieval:
Knowledge Databases:
The goal of day 3 is to Fine-tune a generative model and observe the results. Understand the role of retrieval in augmenting generation. Below are the breakdown of the topics for day 3
Deep Dive into Generative Models:
Hands-on with Generative Models:
Exploring the Interaction Between Retrieval and Generation:
Now, we are getting closer to the goal. The main objective of this day is to Implement a working RAG system on a simple dataset and Gain familiarity with tweaking parameters.Below are the breakdown of the topics for day 4
Combining Retrieval and Generation:
Using Llamaindex’s RAG Pipeline:
Hands-on Experimentation:
The goal of this last day to create a more robust RAG model by Finetuning it and get knowledge about the different types of RAG models that you can explore. Below are the breakdown of the topics for day 5
By following this roadmap, you can learn the RAG system within 5 days depending upon your learning capabilities. I hope you like this roadmap. I usually share Generative AI stuff in the form of a carousel or you can say a bit sized informative post. You can check more carousels on my Linkedin Profile.
If you are looking want to build your RAG from scratch, tune into our FREE course on building RAG system using LlamaIndex!
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