LLM vs. SLM vs. NLU
Why does a bank use NLU, a writer use an LLM, and your phone use an SLM?
Where do they all fit?
Natural Language Understanding
NLU doesn't "chat". It extracts facts. It converts messy human speech into structured data a computer can execute.
How chatbots worked before GPT.
Classifies the purpose.
"Turn on lights" → LIGHTS_ON
Extracts specific parameters.
"Set alarm for 7 AM" → Time: 07:00
If you say something outside its training (e.g., "The sun is too bright"), NLU fails because it has no "LIGHTS_ON" intent mapped to that phrase metaphorically.
Large Language Model
LLMs don't just "extract". They predict. Trained on trillions of words (basically the whole internet), they guess the next word in a sequence.
Think of parameters as "brain connections". GPT-4 has nearly 1.8 trillion.
The engine behind the magic.
It looks at the whole sentence at once, understanding that "bank" means "river bank" vs "money bank" based on context.
It's not deterministic. Asking the same question twice might yield slightly different answers.
Small Language Model
SLMs are condensed versions of LLMs. They sacrifice "knowing everything about everything" to be fast, cheap, and run on your phone.
Examples: Phi-3, Gemma, Llama 3 8B
Distillation & Pruning.
Who is right more often?
Great at reasoning, coding, and obscure facts. But prone to confident Hallucinations.
Extremely accurate in specific domains (e.g., a "Medical SLM"). Fails hard if you ask general knowledge questions outside its training.
100% predictable. If trained to recognize "Balance Inquiry", it will never accidentally write you a poem about balances.
Hardware & Energy.
When you need a brain.
Marketing copy, emails, stories.
Complex refactoring & debugging.
Summarizing 100-page PDFs.
When you need privacy & speed.
Siri/Google Pixel features that work offline.
Processing patient notes locally (HIPAA compliant).
Smart devices with limited RAM.
When you need action.
"Check balance", "Transfer money". (High security/reliability).
"Turn on kitchen lights".
Routing customer support tickets to departments.
Combining powers.
Splits the request.
Merged secure data with creative text.
Configure your project to see the recommended model.
Description goes here.