LLM (Large Language Model)
A model trained on huge amounts of text to predict the next token.
Read definition →Transformer
The neural network architecture behind every major LLM — attention over sequences.
Read definition →Attention Mechanism
How transformers decide which tokens to focus on when generating each output token.
Read definition →Multimodal Model
A model that handles text plus images, audio, or video in one request.
Read definition →Mixture of Experts (MoE)
An architecture where only part of the model activates per token.
Read definition →Distillation
Training a smaller, cheaper model to mimic a larger one's outputs.
Read definition →Fine-Tuning
Continuing to train a base model on your own examples to specialize its behavior.
Read definition →LoRA (Low-Rank Adaptation)
A lightweight way to fine-tune by training small adapter weights instead of the whole model.
Read definition →Quantization
Storing model weights at lower precision (e.g., 4-bit) to save memory and run faster.
Read definition →Temperature
A dial that controls how random or focused a model's output is.
Read definition →Top-p (Nucleus Sampling)
A sampling dial that picks from the smallest set of tokens summing to probability p.
Read definition →Chain-of-Thought (CoT)
Asking the model to reason step by step before answering.
Read definition →RLHF (Reinforcement Learning from Human Feedback)
The training technique that turns a raw LLM into a helpful, safe assistant.
Read definition →Instruct Model
A base model fine-tuned to follow instructions — the "chat" version you actually use.
Read definition →Few-Shot Learning
Showing the model 2-5 examples of the task in the prompt so it learns the pattern.
Read definition →Zero-Shot Prompting
Asking the model to do a task with no examples — just instructions.
Read definition →In-Context Learning (ICL)
How models adapt to new tasks from examples in the prompt, with no weight updates.
Read definition →Diffusion Model
The architecture behind image generators like DALL-E, Midjourney, and Stable Diffusion.
Read definition →GPT-4o
OpenAI's flagship multimodal model — fast, cheap relative to predecessors, and supports vision and voice.
Read definition →Claude Sonnet (Anthropic)
Anthropic's primary workhorse model — strong writing, long context, and reliable tool use.
Read definition →Llama (Meta)
Meta's open-source LLM family — the leading choice for self-hosted and fine-tuned deployments.
Read definition →Gemini (Google)
Google's frontier LLM family — notable for its 2M-token context window and Google ecosystem integration.
Read definition →Vision-Language Model (VLM)
A model that understands both images and text — reads documents, screenshots, and photos.
Read definition →