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.