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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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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.Content provenance (C2PA)Content provenance is a checkable record of where a piece of media came from and how it was edited, attached to the file itself. The C2PA, founded in 2021 by Adobe, Arm, BBC, Intel, Microsoft and Truepic, publishes the technical standard behind it.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.Text watermarkingText watermarking embeds a hidden statistical signal in generated text so that a short span of tokens can later be identified as machine output, while the text still reads normally to people.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.Test-time computeTest-time compute is the computation a model spends while answering a prompt, beyond what training already fixed into its weights. A 2024 study by Snell et al. showed how to spend it adaptively, and how it can stand in for a bigger model.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.Open Source AI Definition (OSAID)The Open Source AI Definition is a 1.0 standard, released by the Open Source Initiative in October 2024, that a model must meet to be called open source: reproducible design, disclosed training data, and freedom to use, modify and build on it.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.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.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]Agent memoryAgent memory is the part of an agent system that stores information beyond the current context window so it can be recalled later. MemGPT (2023) framed the limited context window as the core constraint such designs must work around.Red teamingRed teaming is the practice of actively probing a model for harmful or unwanted behaviour before deployment. A 2022 method by Perez and colleagues automated it by using one language model to generate test cases for another.JailbreakA jailbreak is an adversarial prompt that gets a safety-trained model to produce behaviour its training was meant to prevent. A 2023 analysis by Wei et al. traced these failures to competing objectives and mismatched generalization.Semantic WebThe Semantic Web is a W3C standards effort that uses RDF and OWL to describe data so different applications, enterprises and communities can share and reuse information, moving from research toward practical tools.