RAG for Beginners
First things first
We’ll take a brief look at the under the hood of RAG in a bit, but first let’s take a moment to understand Large Language Models (LLMs) as a general concept. Feel free to skip ahead to the next section if you are comfortable with this topic.
Finding true subject matter depth in LLM responses
Like many others, my first interaction with Large Language Models was fairly superficial - I treated it like an advanced search engine, asking questions and getting answers without really understanding its potential or limitations. I was poking at the technology, fascinated but not quite grasping how to leverage it effectively. The responses had decent breadth but otherwise they were always too general to be truly useful.
Thankfully it wasn’t long before I discovered something that would change my entire perspective: RAG (Retrieval-Augmented Generation). Learning that you could make these general models aware of your specific situation was the moment everything clicked for me, not just technically, but philosophically. I suddenly saw its potential as a tool for enhancing human learning experiences. The ability to feed specific, relevant knowledge into these powerful models was a direct way to address the lack of depth in general model responses.
This is where RAG comes in. RAG, or Retrieval-Augmented Generation, is like giving this LLM assistant a personal library that they can reference in real-time. Instead of relying solely on their training, they can now pull up specific documents, research papers, or notes that you provide them. It’s the difference between asking someone to speak generally about a topic versus having them reference your specific notes or documentation while they answer.
How Does RAG Work?
Remember, the goal is to make the AI system more accurate and relevant to your specific context. Here’s how RAG works:
-
Your Knowledge Base: First, you prepare your documents, notes, or any information you want the AI to access. I have reams of text, articles, and books that I want to use as my knowledge base. Nothing will ever replace reading things cover to cover when I’m learning a new topic, but there are other times where a specific problem requires a specific answer. So, I would love to be able to chat with that information without having to re-read every page.
-
Chunking and Embedding: The system breaks your content into manageable pieces and converts them into a format that the AI can easily search and understand (think of it as creating a smart index for your digital library). There’s a whole world of interesting ways to chunk and embed content, but let’s keep it simple for now.
-
🔍 Retrieval: When you ask a question, the system first searches through your provided information to find the most relevant pieces. “Relevant” is a relative term here, because the AI is smart enough to understand that there might be multiple pieces that are relevant to your question, but it will always pick one, and it won’t necessarily pick the most accurate one. Remember, AI relies on weightage and context, not “accuracy.” Interestingly, your query is embedded much the same way as the content, so the AI can understand your question even if it doesn’t know the answer. This was a big “aha!” moment for me in trying to understand RAG.
-
Generation: The AI then combines its general knowledge with these specific, retrieved pieces to give you an informed, contextual response. This is where the LLM comes back in after the retrieval step and uses the context to generate a response, much like it would if you were querying it the usual way through a web interface or chatbot.
TLDR: RAG is like having a personal AI assistant that understands your specific context and can work with your unique knowledge base.
The Personal Research Revolution
The game-changing aspect of RAG isn’t just its technical capabilities—it’s how it transforms personal research and knowledge management. By feeding an AI model with your own research materials, notes, or documentation, you create a system that:
- Understands your specific context
- Can reference your personal knowledge base
- Maintains accuracy within your domain
- Evolves with your research
Practical Applications
RAG is changing how we work and learn, and there will be ups and downs to that. Imagine you’re a researcher drowning in papers - RAG can help you quickly find and connect relevant information from your personal collection. Or maybe you’re maintaining technical documentation - RAG can help your team get accurate answers based on your specific systems and processes. However, just like any other AI, RAG has its limits - it can’t replace your human brain and you need to be the final judge of the information going in and out of the system.
What I find particularly exciting is how RAG can turn your expertise into an interactive knowledge base. Whether you’re analyzing data or building documentation, you’re not just working with a generic AI - you’re working with one that understands your context and speaks your language.
In my next post, “Hands-On with RAG: From Personal Knowledge to Intelligent Insights”, I’ll walk you through building a real RAG system from scratch, showing you how to apply these concepts in a practical, hands-on way.
Further Technical Information
If you’re thinking about implementing RAG (and I hope you are!), here are some tips from my experience and the experience of others:
Data Quality
Think of your data as the foundation of your RAG system. Just like you wouldn’t build a house on shaky ground, you want your knowledge base to be solid. I’ve learned that taking time to clean and organize your data pays off enormously in the quality of responses you get. If you have access to an AI Agent that can use tools to access your knowledge base, you can tell the agent to maintain data quality by building you a simple dashboard. I also enjoy creating a data cleaning “strategy” with the AI Agent. There is limitless potential here - just make sure you have a solid foundation.
Chunking Strategy
This is where the art meets science in RAG. Breaking down your documents isn’t just about splitting text - it’s about preserving meaning. I like to think of it as creating a conversation-friendly format for your AI assistant. Sometimes a paragraph makes sense as a chunk, other times you might want to keep related concepts together.
Embedding Choice
Embeddings are just your AI’s way of understanding content. Different embedding models have different strengths, kind of like how some people are better at understanding technical writing while others excel at creative content. The key is matching the model to your needs.
Vector Database
Think of this as your AI’s filing system. While it sounds technical (and it is), the choice really comes down to practical questions: How much data do you have? How fast do you need responses? How often will you update your knowledge base? Let these questions guide your choice.
Generating RAG Code with AI
Let’s look at a basic example of RAG in action. Before diving into the code, here’s what we’re working with:
- LangChain: This is a toolkit that helps us connect different AI pieces together
- TextLoader: A simple way to feed our documents into the system
- Embeddings: Converts our text into a format the AI can understand (like translating English to “AI-speak”)
- Vector Store: A special database that helps the AI find related information quickly
Using an open-source model running on my local machine, I was able to generate this common RAG pattern:
from langchain.document_loaders import TextLoader
from langchain.text_splitter import CharacterTextSplitter
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.vectorstores import Chroma
# Load and split your documents
loader = TextLoader('ai_research_notes_on_retrieval_augmented_generation.txt')
documents = loader.load()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
texts = text_splitter.split_documents(documents)
# Create embeddings and vector store
embeddings = HuggingFaceEmbeddings()
vectorstore = Chroma.from_documents(texts, embeddings)
# Perform a retrieval-augmented query
query = "What are the most significant challenges in implementing effective retrieval-augmented generation systems?"
relevant_docs = vectorstore.similarity_search(query, k=3)What’s Actually Happening Here?
Let’s break down what this code is doing:
- First, we’re loading our document (in this case, some research notes about RAG)
- We split it into smaller chunks (1000 characters each with 200 characters of overlap) - this helps the AI digest the information better
- We convert all this text into “AI-speak” using embeddings
- Finally, we store everything in a special database (Chroma) that can quickly find related information
When we ask a question, the system finds the three most relevant chunks of text from our document and uses those to inform the AI’s response. This is what makes RAG responses more focused and accurate than general LLM responses.
⚠️ A Cautionary Note on AI-Generated Code
Multiplying your productivity with AI assistance does not excuse you from actually reading and understanding the output. Always:
- Review each line of generated code
- Read the relevant documentation
- Understand the consequences of each step
Lazily mashing through AI code generation without considering the implications of everything being done on your behalf is a great way to paint yourself into a corner. AI is a tool to assist, not to replace, your understanding.