AI Glossary
Key AI/ML terms explained simply
LLM (Large Language Model)
A large AI model trained on massive text data to understand and generate human language. Examples: GPT-4, Claude, Llama.
Transformer
The neural network architecture behind modern AI. Uses "attention" to understand context in text.
Token
The basic unit AI processes. Can be a word, part of a word, or character. ~1 token ≈ 4 characters.
Fine-tuning
Training a pre-trained model on specific data to improve it for a particular task.
Prompt
The input you give to an AI model. Quality of output depends on quality of prompt.
Embedding
Converting text into numbers that capture meaning. Used for search and similarity.
Temperature
Controls randomness. Higher = more creative, lower = more predictable.
Context Window
Maximum tokens AI can see at once. GPT-4o has 128K tokens.
Zero-shot Learning
AI performs a task without specific training, using general knowledge.
Few-shot Learning
Giving AI a few examples in the prompt to understand the task.
RAG (Retrieval Augmented Generation)
Feeding relevant documents to AI to generate more accurate answers.
Hallucination
When AI generates confident but false information. A known limitation.
Inference
Running an AI model to generate output. Different from training.
Parameters
Internal values the model learns. More parameters usually = more capable.
Quantization
Reducing model size by using fewer bits. Makes it run faster on less RAM.
System Prompt
Instructions that set AI's persona and behavior for all interactions.