If you've been trying to make sense of the AI landscape lately, you've probably encountered a bewildering alphabet soup: LLMs, RAG, AI Agents, MCP. These terms get thrown around like everyone should just know what they mean, but here's the truth most explanations make things more complicated than they need to be.
So let me break it down using an analogy that actually makes sense: the human body.
The Brain: Where It All Begins (LLMs)
Think of a Large Language Model (LLM) as the brain. It's where all the thinking happens, where reasoning takes place, where connections get made. But here's the thing just like your brain operates based on everything you've learned up until this moment, an LLM works with the knowledge it absorbed during training.
It's powerful. It can reason, analyze, create, and solve problems. But it's also isolated in its own skull, so to speak. It only knows what it learned during its training period. Ask it about something that happened last week? It's clueless, just like you'd be clueless about a conversation that happened in a room you weren't in.
This is both the strength and limitation of LLMs. They're brilliant thinkers, but they're working with a fixed knowledge base.
Accessing and Adding Knowledge: RAG Gives the Brain Eyes to Read
Now imagine giving that brain access to a massive library. That's what RAG does.
RAG stands for Retrieval Augmented Generation, which is a mouthful, but the concept is simple: it lets the AI look things up. When you ask a question, the system searches through external documents, databases, or knowledge bases, finds the relevant information, and feeds it to the LLM.[ Vectorized Content]
Think of it as giving the brain the ability to read. You're not changing what the brain fundamentally knows you're just letting it access external knowledge when it needs to. This is how AI systems can answer questions about your company's internal documents, recent news articles, or specialized technical manuals they were never trained on.
The brain is still doing the thinking, but now it's got reference materials to work with.
Taking Action: AI Agents Use Tools[Ears, Eyes, Hands] to Get Things Done
A brain that can read is great, but what if it could also do things?
That's where AI Agents come in. If the LLM is the brain and RAG gives it eyes to read, then an AI Agent is the brain equipped with tools to act. It can plan, make decisions, and most importantly take actions in the real world using those tools.
Need to schedule a meeting? The agent can access your calendar. Want to analyze data? It can run code. Need to send an email? It can do that too. AI Agents move beyond just answering questions they complete tasks, make decisions about what steps to take next, and interact with other systems to get things done.
This is where AI starts to feel less like a chatbot and more like a capable assistant that can actually help you accomplish things.
The Senses: MCP Connects You to the External World
Here's where things get really interesting. You can have all these components the brain (LLM), the books (RAG), the hands (Tools) but they're useless if they can't sense and interact with the world around them.
Enter MCP: the Model Context Protocol. Think of it as your senses your ability to hear, see, and communicate with the external world in real-time.
Just like you call a friend to get information, listen to what's happening around you, or reach out to someone for updates, MCP enables the AI to connect with external systems and get live context. It's hearing what's happening in your Slack channels. It's reaching out to your calendar to see what's scheduled. It's calling an API to get real-time stock prices or weather updates. It's listening to database changes as they happen.
MCP gives the LLM real time awareness of the world beyond its training data. Without it, the AI would be like a person in an isolated room smart, but completely cut off from what's actually happening right now. With MCP, the AI can sense its environment, reach out for fresh information, and stay connected to the living, breathing systems you use every day.
It's the difference between knowing about phones and actually being able to make a call when you need information.
Why This Matters to You
Understanding this stack isn't just academic it changes how you think about using AI.
When you know that an LLM alone is limited to its training data, you understand why it can't tell you what happened yesterday. When you grasp how RAG works, you realize you can give AI access to your specific knowledge without retraining the entire model. When you understand agents, you see opportunities to automate entire workflows, not just get answers. And when you get MCP, you understand how AI can truly connect to your world listening, reaching out, and staying aware of what's happening in real time.
The human body analogy isn't perfect, but it captures something important: modern AI systems work best when all the pieces work together. A brain without tools can only think. Tools without a brain can't accomplish anything meaningful. And all of it is useless if you can't hear, see, or communicate with the world around you.
But when you combine them all? That's when you get something that can actually help you do meaningful work.
The Bottom Line
We're not just building smarter chatbots anymore. We're building coherent AI systems that mirror how we actually function thinking, learning, acting, and connecting it all together.
The next time someone throws around terms like "RAG pipeline" or "agentic workflows," you'll know exactly what they're talking about. More importantly, you'll understand how to actually use these technologies to solve real problems.
Because at the end of the day, the best technology isn't the most complex it's the kind that makes sense, the kind that works the way we do.
And that's exactly what we're building.
Thanks
Sreeni Ramadorai
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