Understanding AI Without Technical Jargon

"So what exactly does that AI do?" If you can answer this without technical jargon, you truly understand it.

You Don't Need to Know the Tech — But You Need to Judge It

There's an uncomfortable moment for every AI project decision-maker — CEO, executive, team lead. The tech team is explaining something, and you hear the words but don't grasp the meaning.

"We'll apply RAG on top of an LLM, and fine-tune if necessary."

You nod, but your mind goes blank. Then you approve budgets, select vendors, or set project direction in that state.

This is dangerous. You don't need to build the technology yourself, but you need to understand what it does. That's the only way to ask the right questions and filter out bad proposals.

This article explains the most common AI project concepts without any technical jargon.

Large Language Models — A New Hire Who Has Read Everything

The core of AI like ChatGPT and Claude — the large language model (LLM) — is essentially a new employee who has read nearly everything on the internet.

This new hire has remarkable abilities. They can speak convincingly on any topic, summarize documents, translate languages, even write code. After all, they've read thousands of books and hundreds of millions of documents.

But there are critical limitations.

They've never read your company's internal documents. They have abundant general knowledge from the public internet, but they don't know your product specs, your customer histories, or your team's processes.

Reading and knowing are different things. They've read a lot, but can't judge what's accurate and what's wrong. So occasionally they give incorrect answers with complete confidence. This is called "hallucination."

They don't know what happened yesterday. Their training only goes up to a certain point, so they're weak on the latest information.

That's why deploying this new hire directly into real work causes problems. Additional mechanisms are needed.

RAG — Handing Reference Materials to the New Hire

RAG (Retrieval-Augmented Generation) looks complicated, but the essence is simple.

Making the new hire look up relevant documents before answering.

When you ask "What's our company's refund policy?", instead of answering from memory, the AI first searches your internal documents for refund policy content. Then it crafts an answer based on what it found.

This improves two things.

Accuracy goes up. Because answers are based on actual documents, not general knowledge from memory.

Sources can be verified. It can say "This answer is based on page 3 of the refund policy document," making it easy for people to verify.

However, there are limits. If your documents are a mess, search results are a mess, and answers are a mess. It boosts AI performance, but it doesn't fix your document quality for you.

The question decision-makers should ask: "Are the documents we'd give AI to reference actually well-organized?"

Fine-Tuning — Retraining the New Hire in Your Company's Way

Fine-tuning means additionally training a general-purpose AI to fit your specific needs.

Continuing the analogy: the new hire has rich general business knowledge, but can't write in your industry's specialized terminology or your company's tone of voice. Fine-tuning is the process of repeatedly teaching them "this is how we do things here."

For example, if you're building a customer service AI, you'd train it on thousands of your company's past support conversations so it responds in your tone.

But fine-tuning is expensive and time-consuming. And it only works with sufficient data. So the realistic order is:

Stage 1: Just use a general-purpose AI as-is (cost: nearly zero)

Stage 2: Use RAG to reference your documents (cost: low to medium)

Stage 3: Consider fine-tuning only if still insufficient (cost: high)

Most companies get sufficient results at Stage 2. Cases requiring Stage 3 are rarer than you'd think.

The question decision-makers should ask: "Is fine-tuning really necessary, or is RAG enough?"

AI Agents — Giving the New Hire Authority to Act

This is the concept you hear about most lately. AI agents go beyond simply answering questions — they're AI that judges and acts on its own.

If previous AI was "a consultant who answers when asked," an agent is closer to "a worker you assign tasks to and they handle it."

For example, if you say "Schedule a meeting for next week," the agent checks attendees' calendars, finds open slots, sends meeting invitations, and attaches necessary materials. No need for a person to direct each step — it autonomously executes the entire process.

Powerful, but risky. If it misjudges, it also misacts. It could send wrong emails, place incorrect orders, or transmit sensitive data externally.

That's why the key to agent adoption isn't technology — it's authority design. You must clearly define what the agent can do autonomously and what requires human approval.

The question decision-makers should ask: "What's the boundary of what this agent can decide on its own?"

How the Four Concepts Relate

Here's the summary.

LLM (Large Language Model): A well-read generalist. The base engine.

RAG (Retrieval-Augmented Generation): A mechanism to make that generalist reference your materials. The most common and practical customization.

Fine-tuning: Retraining that generalist in your company's way. Only when needed, and carefully.

Agent: Giving that generalist authority to execute. Most powerful, but requires the most caution.

Most AI projects start with the LLM + RAG combination. After confirming sufficient value there, expanding to fine-tuning or agents as needed is the realistic path.

It's Not About Knowing Jargon — It's About Knowing the Questions

After reading this, you don't need to be able to technically explain what an LLM is or how RAG works. Instead, when your tech team or vendor brings up these terms, you should be able to ask the right questions.

"We'll use an LLM" → "Which model, and how do you manage hallucination?" "We'll apply RAG" → "Is our documentation in good enough shape to support this?" "We need fine-tuning" → "Can't RAG handle it? Do we have enough data?" "We'll build an agent" → "How is the boundary between autonomous execution and human approval defined?"

It's not about understanding technology — it's about judging technology. That's AI literacy for decision-makers.