What Is a Language Model?
When you chat with an AI coding assistant, you're interacting with a Large Language Model (LLM). Understanding what these models actually are — and aren't — helps you use them more effectively and set realistic expectations.
How Language Models Work
An LLM is a neural network trained on massive amounts of text. During training, it learns patterns: grammar rules, factual associations, reasoning patterns, coding conventions, and much more. When you send a prompt, the model predicts what text should come next based on those learned patterns.
This is fundamentally sophisticated pattern matching, not thinking. The model doesn't understand your code the way you do — it recognizes patterns similar to what it saw during training and generates plausible continuations.
Training vs Inference
These are two distinct phases:
Training (done by AI companies):
- Feed massive text datasets (books, code, websites)
- Model learns patterns and relationships
- Requires enormous compute (millions of dollars)
- Done once to create the model
Inference (what you do):
- Send a prompt to the trained model
- Model generates a response
- Relatively cheap per request
- Uses frozen, pre-trained weights
You interact only with inference. The model's knowledge is fixed at training time — it can't learn from your conversations or access new information unless given through RAG or similar techniques.
Why They Seem Intelligent
LLMs appear remarkably capable because they've absorbed patterns from most of human written knowledge. They can combine these patterns in novel ways, explain complex topics, and generate working code.
But this capability has limits. Models excel at common patterns they've seen many times. They struggle with truly novel problems, edge cases, and anything requiring genuine reasoning rather than pattern recognition.
Key Limitations to Remember
Knowledge cutoff — Models only know what existed in their training data. They can't tell you about recent events or your private codebase.
Hallucinations — Models generate plausible-sounding text even when they don't "know" the answer. They'll confidently cite nonexistent documentation or invent API methods.
No uncertainty awareness — Models can't reliably tell you when they're guessing. Confidence in the response doesn't indicate accuracy.
Think of an LLM as very sophisticated autocomplete trained on the internet — incredibly useful, but requiring human judgment to verify and apply its outputs.