TOPIC · ENGLISH EDITION
Concepts
Core technical concepts used across modern AI systems.
AI Agent
An AI system that selects and executes actions over multiple steps to pursue a goal.
Attention
A mechanism that lets a neural network weigh the relevance of different positions when computing a representation.
Context Window
The amount of tokenized input and generated text a model can consider within one inference context.
Diffusion Model
A generative model that learns to reverse a gradual noise process to create data such as images.
Embedding
A numerical vector representation used to encode semantic or structural properties of data.
Fine-tuning
Additional training that adapts a pre-trained model to a narrower task, domain, or behavior.
GPU
A highly parallel processor widely used to train and run modern machine-learning models.
Inference
The process of running a trained model on new input to produce predictions, embeddings, or generated output.
KV Cache
A cache of attention keys and values used to avoid recomputing earlier context during autoregressive generation.
Knowledge Distillation
A training method in which a smaller student model learns from the outputs or representations of a larger teacher model.
Latency and Throughput
Two core serving metrics describing response delay and the amount of work a system completes over time.
LoRA
Low-Rank Adaptation, a parameter-efficient technique for adapting large neural networks.
Mixture of Experts (MoE)
A model architecture that routes each input to a subset of specialized parameter blocks called experts.
Multimodal AI
AI systems that process or generate more than one type of data, such as text, images, audio, or video.
Quantization
A technique that represents model weights or activations with lower numerical precision to reduce resource use.
RLHF
Reinforcement Learning from Human Feedback, a method for aligning model behavior with human preferences.
Retrieval-Augmented Generation (RAG)
A method that retrieves external information and supplies it to a generative model as context.
Tokenization
The process of converting text or other input into discrete units that a model can process.
Transformer
A neural network architecture that processes sequences primarily through attention mechanisms.
Vector Search
Search that retrieves items by similarity between numerical vector representations.