What Shapes an AI Model
When companies build AI models, they make a few key decisions that affect how the model works:
Architecture Choice
Most modern language AI uses what's called a "transformer" architecture. This design is special because it:
Can look at an entire sentence at once (not just one word at a time)
Pays "attention" to important words when generating responses
Handles long texts better than older designs
It's like the difference between reading a book by scanning all pages simultaneously vs. reading one word at a time.
Size Matters
Models come in different sizes, measured by "parameters" (basically the model's brain cells):
Small models (1-10B parameters): Faster, cheaper, easier to run on normal computers
Medium models (10-70B parameters): Better quality, still somewhat practical
Large models (70B+ parameters): Highest quality, but expensive and slow
The biggest models (like GPT-4 or Claude) have hundreds of billions of parameters. They're smarter but need specialized hardware.
Context Length
This is how much text the model can "remember" at once:
4K context (older models): About 3,000 words or 12 pages
128K context (newer models): About 100,000 words or 400 pages
Longer context means the model can work with bigger documents but uses more memory.
Why This Matters For Using AI
Even if you don't create models, knowing these basics helps you:
Pick the right model for what you need (balancing quality with cost and speed)
Understand why some models are better for certain tasks
Recognize limitations (like why the model might forget earlier parts of a long chat)
Make smarter choices about using them (some models won't work on your laptop!)
It's like buying a car → you don't need to know all about the engine, but knowing the difference between a small car and a truck helps you choose the right one.