RAG has been everywhere lately. Every AI product demo mentions it, every startup claims to use it, and every tutorial starts with the same vague explanation. But when I actually tried to learn the full picture -- from embeddings to vector databases to production architecture patterns -- I found the resources scattered across dozens of blog posts, papers, and documentation pages. There was no single place that connected all the dots in a way that actually stuck.

So I built one. RAG Mastery is an interactive, single-page website that teaches you everything about Retrieval-Augmented Generation -- from the basic concept to advanced architecture patterns -- with animations, live demos, and a quiz to test yourself.

Why Another RAG Resource

The problem with most RAG tutorials is they either go too shallow or too deep too fast. They'll show you a LangChain snippet and call it a day, or they'll jump straight into re-ranking algorithms without explaining why you'd need one. I wanted something that built understanding layer by layer -- the way you'd actually learn it if someone was walking you through it on a whiteboard.

I also wanted it to be interactive. Reading about how chunking works is one thing. Watching the same text get split differently by four different strategies -- fixed-size, sentence-based, semantic, and recursive -- is something else entirely. That visual comparison made the tradeoffs click for me in a way paragraphs of text never did.

What's Inside

The site covers nine sections, each building on the last. It starts with the fundamental question -- what is RAG and why does it matter -- with a side-by-side comparison of LLMs with and without retrieval augmentation. The contrast makes it immediately obvious why this technique exists.

The pipeline section is probably the centerpiece. It's an interactive 8-step walkthrough from document ingestion all the way to generation. Click any step -- Ingest, Chunk, Embed, Index, Query, Retrieve, Augment, Generate -- and you get a detailed explanation with relevant tags. The progress bar fills as you move through, and previous steps show as completed. It makes the full flow tangible.

Then there's the chunking demo, where you can see the same source text split four different ways. Fixed-size chunking breaks mid-sentence (which is why it's usually not great). Sentence chunking preserves meaning but produces uneven sizes. Semantic chunking groups by topic shifts. Recursive chunking tries paragraph boundaries first, then falls back to sentences. Watching these side by side was an eye-opener even for me while building it.

The embeddings section includes a 2D vector space visualization. You can see how words like "cat", "dog", and "bird" cluster together in one region while "computer", "database", and "algorithm" cluster in another. It's a simplified illustration, but it communicates the core concept of semantic similarity in vector space better than any definition could.

I also included detailed cards for six vector databases -- Pinecone, Weaviate, ChromaDB, Milvus, Qdrant, and pgvector -- each with their features, best use cases, and scale capabilities. And a section comparing LLMs (Claude, GPT-4o, Llama 3, Mistral) for RAG with their context windows and strengths.

The Architecture Patterns

This section was the most fun to build. RAG isn't just one thing -- it's evolved into several distinct patterns, and understanding the spectrum matters. I created a tabbed interface showing four patterns: Naive RAG (basic retrieve-then-read), Advanced RAG (with query rewriting and re-ranking), Modular RAG (plug-and-play components), and Agentic RAG (autonomous agents that decide when and how to retrieve). Each has a visual pipeline diagram, description, and honest pros/cons.

The Learning Roadmap

I structured a six-stage path from foundations to cutting edge. Each stage is an expandable card with specific skills to learn, rough timelines, and curated resources. Stage 1 covers NLP basics and transformers. Stage 2 dives into embeddings and vector stores. Stage 3 is your first RAG pipeline. Stages 4 through 6 cover advanced techniques, production deployment, and bleeding-edge research like Graph RAG and Self-RAG. It's the roadmap I wish I had when I started.

Zero Dependencies

The entire site is built with pure HTML, CSS, and JavaScript. No React, no build step, no npm install. Just open the file in a browser. The particle background runs on Canvas API. Scroll animations use Intersection Observer. The interactive demos are vanilla JS event handlers. The chunking strategies are implemented as pure functions.

I chose this approach deliberately. The site is about learning fundamentals -- it felt wrong to bury it under layers of tooling. Plus, it loads instantly and works everywhere.

The Quiz

At the bottom there's a 10-question quiz that tests everything from basic definitions to advanced concepts like HyDE and HNSW indexing. Each question gives instant feedback with an explanation -- whether you get it right or wrong, you learn something. I've watched a few people take it and the average score on first attempt is around 6 or 7 out of 10, which tells me the difficulty is about right.

What I Learned Building This

Building a teaching tool forces you to understand things more deeply than just using them. I had to think carefully about the order of concepts, what analogies actually work, and where interactive elements add real value versus being gimmicks. Not every section needs animation -- sometimes a clean comparison table communicates more than a flashy diagram.

The project was also a reminder of how far vanilla web technologies have come. CSS animations, canvas, intersection observers, SVG -- you can build genuinely impressive interactive experiences without a single dependency. No React, no build step, and it still feels polished.

The code is on GitHub. If you're learning RAG or know someone who is, give it a look.