AI Fundamentals for Leaders: 8 Concepts You Need to Know

The Meeting That Changed Everything

I was sitting in the meeting, observing. Someone said: "We need an AI solution!" Someone else nodded: "Yes, which LLM should we use?" A third person: "But tokens are expensive, right?" Fourth person: "Or do we need RAG? Or maybe an agent?"

I looked at the faces. Everyone was nodding. Everyone looked intelligent. But the eyes... the eyes told a different story.

I looked to the right: an uncertain glance. I looked to the left: quiet nodding, but no understanding behind it. And then I realized something. Nobody really knows what we're talking about. Just me? No. We were all sitting there, using the words, but their meaning... was blurred.

That moment changed everything.

Why I'm Writing This Post

Since then, I've been in similar situations countless times. And I realized a few things:

Everyone uses these words - in meetings, presentations, on LinkedIn
Few truly understand them - but nobody wants to admit it
The shame of ignorance - holds us back from asking
Decision-making suffers - if we don't understand the concepts, how can we decide correctly?

So I sat down and created a map. Not a complicated document filled with tech jargon. But a simple guide for those who - like I once was - have heard these terms but want to finally see clearly.

Are you ready? Let's go.

(Quick note: This is not a complete AI encyclopedia - but a map outline. The most important concepts you encounter day to day. In a nutshell, but comprehensive. Enough for you to navigate confidently.)

AI ≠ LLM: The Forest and the Tree

What people think: "AI = ChatGPT, Gemini, Claude and their peers, right?"

What it actually is: Artificial Intelligence is a vast forest, full of different tree species. LLM (Large Language Model) is just ONE tree species in this forest.

The AI family members:

Computer Vision - image recognition, face identification
Predictive Systems - forecasting, pattern recognition
Robotics - autonomous systems, physical AI
Speech Recognition - voice-based AI
And yes, LLMs - language models

Why this matters to you as a leader: When building an AI strategy, you're not just talking about chatbots. You might need computer vision in production, predictive models in finance, and LLMs in customer service. Different tools for different problems.

So when we talk about AI, it's worth clarifying: what TYPE of AI are we thinking about?

Now that you see the forest, let's talk about that tree getting the most attention right now: LLMs. But first, we need to understand how these systems "think." And that's where this comes in...

Token: The Invisible Currency

What people think: "Token = word" or "some crypto thing, right?"

What it actually is: A token is the building block of LLM thinking. NOT a word. But a word fragment, character, or concept - how the AI "sees" language.

Look at this:

"Budapest" = 2-3 tokens (Buda + pest + possibly a language marker)
" " (space) = 1 token
"AI" = 1-2 tokens
"I love coffee" = 4-5 tokens

The LLM doesn't "read" like we do. It breaks text into tokens and works with those.

Why this matters to you as a leader: When calculating AI costs, they charge tokens, not words. When planning capacity (context window = how many tokens it can handle at once), think in tokens. A 100,000 token context window is NOT 100,000 words - it's around 75,000 words.

That's why it's important to clarify: how much does it cost PER TOKEN, not per word or character!

Now we understand that AI thinks in tokens. But what exactly is an LLM?

LLM: The Language Virtuoso

What people think: "A smart search engine" or "it knows the answers"

What it actually is: A Large Language Model is a massive pattern recognition system that learned language structures and patterns from enormous amounts of text. It doesn't "know" the truth - it "understands" language patterns.

Imagine: it read billions of pages of text and learned:

What typically follows what
Which words go together
Which structures work in different contexts

But: it has no sense of reality, no memory, doesn't "understand" the world - only language patterns.

Why this matters to you as a leader: This explains why you need to be careful with LLM responses. It doesn't "know" things - it follows patterns. That's why it sometimes hallucinates (makes things up because it "sounds right" based on patterns). And that's why human oversight is essential.

That's why prompt engineering is needed - we don't command it, but give it context that helps it recognize the right patterns!

And here we enter a crucial area: how do we communicate with this pattern recognizer?

Prompt & Prompt Engineering: The Art of Conversation

What people think: "Just type what I want"

What it actually is: A prompt isn't just a question or command. It's the complete context you provide:

Role (e.g., "You are an experienced software developer")
Task (what needs to be done)
Context (what's the background, what's the goal)
Examples (I expect the response in this format)
Constraints (what to watch for, what NOT to do)

Prompt engineering = communicating the way the LLM "thinks". Providing patterns and context that guide it in the right direction.

Example - weak prompt: "Write an email"

Example - good prompt: "You are a customer success manager. Write a short (max 3 paragraphs) email to a customer who hasn't used our service for 2 weeks. Your goal: understand what the obstacle is and offer help. Tone: helpful, not sales pressure."

Why this matters to you as a leader: Like a leadership briefing - the better the context, the clearer the task, the better the result. Prompt engineering isn't tech magic, but a communication skill.

It's like when you brief your team - clear context, clear expectations = better results!

But the prompt doesn't exist in isolation. The LLM needs "memory" - and this leads us to the next concept...

Context Engineering: The Architecture of Memory

What people think: "It will remember previous conversations"

What it actually is: The LLM has no memory. It only "sees" what you PUT INTO the context. With each response, you resend the entire conversation so far (or at least the relevant parts).

Context engineering = consciously building the context:

Relevant parts of previous conversation
Related documents
Knowledge base details
User preferences

All this "fits" in the context window - the maximum number of tokens the LLM can handle at once.

Example: Claude Sonnet context window: ~200,000 tokens = about 150,000 words = about a large book.

Why this matters to you as a leader: Every new chat/session = blank slate. If you want it to "remember" someone/something, you need to BUILD that into the context. That's the essence of context engineering.

That's why in a new chat you have to explain everything again - there's no memory, only what we put in!

And now comes magic that connects context engineering with your own data...

RAG: Retrieval Augmented Generation - The Librarian

What people think: "We'll teach it our own data" (fine-tuning)

What it actually is: RAG = not teaching, but retrieval in real-time. Like a smart librarian:

You ask: "What's our company's vacation policy?"
RAG system retrieves the relevant documents (HR handbook)
LLM receives these documents in the context
LLM responds based on the documents you provided

It didn't learn about them beforehand - it got them now, in real-time.

RAG vs. Fine-tuning:

RAG: Retrieve knowledge in real-time (fast, flexible, always fresh)
Fine-tuning: Retrain the model (slow, expensive, by the time it's ready, data is outdated)

Why this matters to you as a leader: If you want to incorporate your own company knowledge, you probably need RAG, not fine-tuning. Faster, cheaper, and always up-to-date.

Like an assistant who looks everything up beforehand before answering - doesn't need to memorize, just needs to know where to find it!

Now let's jump up a level: what if we want not just answers, but action?

Agentic AI: The Acting Intelligence

What people think: "A chatbot that answers questions"

What it actually is: An agent doesn't just answer - it acts. It thinks independently, uses tools, works in multiple steps.

Difference:

Chatbot/LLM: You ask → it answers
Agent: You give a goal → it plans → uses tools → executes → checks

Example:

❌ Chatbot: "What's the temperature in the room?"
✅ Agent: "Optimize the climate!" → Agent checks the temperature → compares with preferences → adjusts the AC → verifies the result

Why this matters to you as a leader: Agentic AI doesn't just provide information - it automates workflows. You don't ask it, you give it a task, and it executes.

We're not in "question-answer" mode, but in "goal-execution" mode!

And finally, the secret of the agent's magic: how does it get tools?

MCP Server: Model Context Protocol - The Bridge

What people think: "Some complicated tech thing in the background"

What it actually is: MCP (Model Context Protocol) is a unified protocol that defines how you give tools to AI agents.

Analogy: the USB port Remember when every device had a different connector? Then came USB, and everything connects the same way. MCP is the same - a unified way to give tools to agents.

What MCP does:

Unified interface to databases, APIs, file systems
Security layer - controls what the agent can access
Simple integration - no need to write a new protocol for every tool

Example: Agent + MCP server → access to:

Google Drive files
Company database
Email system
Calendar ...all through the same protocol.

Why this matters to you as a leader: MCP enables agents to "use" company systems. Without it, every agent integration would be separate development.

Like USB - one port, every device!

Now You Know

Imagine: you're sitting in the next meeting. Someone says: "Let's use AI!"

And you calmly ask back:

"What type of AI are you thinking of? LLM-based customer service? Predictive analytics? Agentic workflow automation?"
"If LLM, what's the context window? How many tokens does it handle?"
"Do we need RAG for company knowledge, or is good prompt engineering enough?"
"Do we want an agent, or is a chatbot enough?"
"Do we have MCP infrastructure to give it tools?"

And you see the faces. This time YOU are the one who knows what they're talking about.

Not because you became a tech guru. But because you understood the map. You know where you are in the AI forest, and you know what questions to ask.

This confidence helps you make decisions. Helps you communicate with the tech team. Helps you understand when to look for which solution.

From now on, you won't be at the mercy of buzzwords.

From now on, you see clearly.

Which concept gave you the "aha!" moment? Share it with me!