LLMs Aren’t What I Thought They Were
Source: Dev.to
Misconceptions About LLMs
I kept seeing “LLM” everywhere.
At first, I assumed it was just another fancy name for ChatGPT — and that assumption slowed everything down.
In my head, an LLM was:
- a magical AI brain
- something only researchers build
- tightly coupled to one specific task
That seemed reasonable. “Large Language Model” sounds intimidating, but this mental model created friction:
- I didn’t know where it fit in an app
- I couldn’t tell what part I was actually using
Everything felt more complex than it needed to be.
The Shift in Thinking
The shift happened when I stopped thinking of LLMs as products or infrastructure. An LLM is not ChatGPT; ChatGPT is a product built on top of an LLM. Models like GPT and Gemini power products such as ChatGPT. That single distinction changed how I thought about AI.
At its core, an LLM is a system designed to do one thing extremely well: predict the next word. It doesn’t understand language the way humans do—that’s why it feels intelligent.
Two Key Characteristics
-
“Large” means data, not size
LLMs are trained on huge datasets—books, articles, websites—capturing patterns of language. -
They’re general‑purpose
Unlike traditional ML models built for one task, the same engine can power:- chat interfaces
- code assistants
- summarizers
- explainers
The same engine, different products.
LLMs vs. Applications
Think of frontend tools: React isn’t a product; it’s a library that powers products. In the same way:
- LLMs aren’t apps – they’re engines behind apps.
- What you experience depends entirely on:
- the interface
- the constraints
- the instructions layered on top
Under the Hood
Under the hood, LLMs work by repeatedly predicting the next word in a sequence using transformer architectures. You don’t need to understand transformers to use LLMs.
I felt intimidated because I misunderstood what they were. Once I saw them as powerful prediction engines, they became approachable.