I’m trying to get into RAG and want to understand how it functions from the ground up. My goal is to implement it with smaller language models in the 4-7 billion parameter range. I’m looking for comprehensive learning materials that can help me grasp the concepts without being too overwhelming. Does anyone know of quality educational content, step-by-step guides, or practical tools that would be suitable for someone just starting out? I’d really appreciate any recommendations you might have. Video content would be especially helpful since I learn better that way, but I’m open to any format that explains things clearly.
Been there. When I started learning RAG two years ago, I wasted time diving into research papers first. Big mistake.
What clicked was building a simple document Q&A system for our work docs. I used ChromaDB as the vector store and Flan-T5 3B just to see how everything connected.
Pinecone’s learning center was surprisingly helpful. Their RAG tutorials are practical and show you common gotchas like context window management and retrieval ranking.
Here’s what really helped me understand retrieval: actually look at what gets retrieved before it hits the model. Most tutorials skip this debugging step. Use basic cosine similarity visualization to see if your chunks make sense.
Try different chunk sizes early. I spent weeks with terrible answers before realizing my chunks were too small for the 7B models I was using. Bigger models handle longer contexts better, so experiment with 500-1000 token chunks.
Start simple with one document type. PDFs, markdown, whatever you have. Get that working before handling multiple formats.
the deeplearning.ai rag course is solid for beginners. covers embeddings, vector search and retrieval basics w/o getting 2 technical. also check out llamaindex docs - they got great tutorials for smaller models like what ur looking for.
Skip the theory - jump straight into building stuff. That’s how I actually learned RAG.
Start with Hugging Face’s free RAG course. It covers vector databases and retrieval mechanisms, plus their notebooks let you mess around with smaller models like Mistral 7B or Llama2 7B.
For videos, Sam Witteveen’s YouTube channel is gold. He walks through RAG implementations step by step, no academic BS.
Here’s what really sped up my learning though - automating everything. Manually setting up vector databases, embedding generation, retrieval logic, and model inference gets old fast when you’re experimenting.
I use Latenode for my RAG workflows. It handles data preprocessing, connects to vector stores, manages embedding model API calls, and chains it all together. I can focus on understanding concepts instead of fighting infrastructure.
You can prototype different approaches quickly, test retrieval strategies, and A/B test model combinations. The visual workflow builder shows exactly how data moves through your pipeline.
Automate the boring stuff and you’ll learn way faster because you can actually iterate and experiment.
I got the most out of mixing theory with actually building stuff. The Anthropic cookbook has solid RAG tutorials that focus on implementation, not just concepts. Start with LangChain’s docs - their RAG examples are well-documented and you can easily drop in smaller models like CodeLlama 7B or Phi-3. For videos, check out Machine Learning Street Talk’s RAG architecture episodes. They dive deep into retrieval mechanisms and chunking strategies that most beginner tutorials skip. Here’s what I wish someone told me earlier: your embedding model matters just as much as your language model. Play around with different embedding approaches using sentence-transformers before you mess with fine-tuning the generation part.