Core Concepts
12 concepts
Scaling Laws for LLMs
Empirical patterns showing that LLM capabilities improve predictably as model size, training data, and compute increase — enabling reliable planning of AI investments
AI Hallucination
When an LLM confidently generates false or fabricated information
AI Inference
The process of running a trained LLM to generate output from input
Fine-Tuning
Training a pre-trained LLM further on domain-specific data to specialize its behavior
Temperature in AI
A parameter controlling the randomness of LLM output — lower values produce consistent results, higher values increase creativity
Top-p (Nucleus) Sampling
A decoding method that samples from the smallest set of tokens whose cumulative probability exceeds a threshold p — adapting candidate pool size to model confidence
Context Window
The maximum number of tokens an LLM can process in a single request
Large Language Model (LLM)
A neural network trained on massive text data to understand and generate human-like language
Neural Network
A network of interconnected artificial neurons that learns patterns from data — the foundational architecture behind all modern AI
Prompt
The input text or instructions given to an LLM to generate a response
Token in AI
The smallest unit of text an LLM processes — approximately 4 characters or 0.75 words
Embedding
A numerical vector that captures the semantic meaning of text, enabling similarity search