From raw tokens to autonomous agents — every concept made visual, interactive, and actually understandable.
LLM → RAG → MCP → Agents — the full stack, visually explained with real flows and examples.
RAG solves the #1 problem with LLMs: hallucination. Instead of guessing, the AI first retrieves relevant documents, then generates an answer grounded in real data.
MCP is like USB-C for AI tools. Instead of every AI app needing custom integrations, MCP provides one universal protocol. Built by Anthropic, adopted by OpenAI, LangChain, and others.
| System | What It Does | Memory | Tools | Best For | Example |
|---|---|---|---|---|---|
| LLM | Predicts next token from training data | Only within context window | ❌ | Text generation, Q&A | ChatGPT answering a question |
| RAG | LLM + real-time document retrieval | Retrieved docs per query | 🗄️ Vector DB | Factual Q&A, knowledge bases | Company chatbot on internal docs |
| AI Agent | LLM that decides to use tools | Session context | ✅ Any MCP tool | Research, coding, automation | Agent that writes + tests code |
| Agentic AI | Agent that loops, reflects, improves | Long-term vector memory | ✅ Multiple tools | Complex multi-step goals | Auto-booking flight + hotel + visa |
From the abstract idea of AI to the product in your pocket — 10 nested layers, each building on the last.
Interactive 10-layer diagram loading…
5 layers that power every AI system on Earth — from raw electricity to the chat interface you use daily. Click any layer to reveal the full story.
"AI is not software. It is a new kind of computing — one that requires an entirely new stack, from the ground up."— Jensen Huang, CEO NVIDIA · GTC 2024
Interactive pyramid loading…
Click any node → full explanation + real-life use cases + ready-to-use prompt
Interactive mind map loading…
Click a token. Watch it gather context from every other word — step by step, math made visible.
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Click any token chip once it appears.
Focused on: —
Focused on: —
— is now enriched with context.
Autonomous systems that plan, act, observe, and improve themselves in a loop until a goal is achieved.
Unlike a chatbot that answers once and stops, an Agent loops. It plans steps, executes them, observes results, and decides whether to continue or change course.
AI safety is not a single filter. It is a layered system where each stage removes specific risks — transforming raw input into reliable, controlled output. Click any layer.
Even after passing through all 13 layers, no system is perfectly secure. Click each risk to understand it.
Top GitHub repositories and free courses hand-picked for your AI literacy journey.
Click any card to see why it matters today and how people are using it.
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Satyam's personal collection of notes, summaries and curated PDFs. Access may require a request — it's a personal Google Drive folder.