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Concepts
Concepts
How AI works, in plain language: from the artificial neuron to the Transformer, pre-training and reasoning models.
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TokenizationTokenization splits text into the tokens a language model actually reads; most modern systems do it with byte pair encoding, a 1994 compression method adapted for translation in 2015.Attention mechanismAttention scores how important each element of a sequence is relative to the others and turns those scores into weights, letting a model read any part of its input directly. It is the operation at the core of the transformer.Attention and self-attentionAttention lets a neural network weigh the parts of its input by relevance. Self-attention applies this idea inside a single sequence, so each token gathers information from every other token. It is the core operation of the Transformer.Positional encodingA component of sequence models, especially transformers, that encodes each token's position as a vector, giving order-blind attention a sense of sequence; its forms range from the original transformer's sinusoids to RoPE and ALiBi.TransformerThe Transformer is a neural network architecture introduced in 2017 that relies on self-attention instead of recurrence or convolution. Most of today's large language models are built on it.Retrieval-augmented generationRetrieval-augmented generation (RAG) lets a language model fetch relevant documents from an external store and put them into its prompt before answering, so it can use knowledge beyond its training data and cite where an answer came from.Instruction tuningInstruction tuning is a supervised fine-tuning stage that trains a large language model to follow user instructions; the FLAN paper showed it substantially improves zero-shot performance on unseen tasks.Speculative decodingSpeculative decoding speeds up text generation from large autoregressive models: a smaller model proposes several tokens and the large model checks them in parallel, producing identical output faster.Prompt injectionPrompt injection is an attack in which text inside a model's input overrides its original instructions, letting an adversary hijack a language-model application — including, as researchers showed in 2023, through content the application retrieves on its own.Computer useComputer use is an Anthropic capability, released in public beta in October 2024, that lets an AI model operate a computer the way a person does — reading the screen, moving a cursor, clicking and typing — so it can drive ordinary software built for humans.AI agentAn AI agent is a system that perceives its environment and acts autonomously toward goals. The term covers everything from a thermostat to LLM-driven software that directs its own tool use.Multi-agent systemA multi-agent system is a computing system made of several interacting intelligent agents that coordinate to solve problems too hard for a single agent. Recent frameworks built on large language models let such agents talk to each other.World modelA world model is a machine learning system that learns an internal representation of an environment and predicts how it changes in response to actions, letting agents plan inside a learned simulator instead of by trial and error.Context windowA context window is the full span of text a model can refer to while producing a response, measured in tokens and including the response itself. Its size, and how evenly a model uses it, shape what the model can do.Long contextLong context is a model's capacity to take in and work over very large inputs — documents, images, recordings — in a single request. Systems now reach millions of tokens, though finding information inside that window is a separate skill.KV cacheA KV cache keeps the key and value vectors a model has already computed for earlier tokens, so each new token reuses them instead of recomputing. Its memory footprint is what makes high-throughput serving hard to scale.Synthetic dataSynthetic data is training material written by a model rather than collected from people or the web. In the phi-1 experiments a small code model trained partly on model-generated "textbooks" reached competitive benchmark scores, suggesting data quality itself shapes capability.Multimodal modelsA multimodal model handles more than one kind of input or output, such as images together with text, inside a single model. Work on CLIP and Flamingo showed how such models are trained and how they can be steered toward new tasks.Tool callingTool calling lets a language model hand a sub-task, such as arithmetic or search, to an external program and use the result in its answer. Toolformer showed in 2023 that a model could learn when and how to call tools on its own.Planning in AI agentsPlanning in AI agents is the process of breaking a goal into intermediate steps, choosing actions, and revising the plan as results arrive, rather than producing an answer in one pass.[^1][^2]