prompt
In plain wordsThe message or instructions you give an AI system — a question, a request, some examples or a document — that it responds to. Clearer prompts generally get better results.
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Language models
The input text (and, for multimodal models, images, audio or files) given to a generative language modellanguage model, n.: A system that has learned how likely different sequences of words are, so it can predict the next word in a text and, by doing that repeatedly, write text of its own., which the model conditions on to produce its output. A prompt may combine instructions, background context, retrieved documents, examples of the desired behavior, the user's question and constraints on format; in chat systems it is assembled from a system promptsystem prompt, n.: Background instructions given to a chatbot before the conversation starts, setting its role, rules, tone and tools. Users usually never see it, but it shapes every reply., earlier conversation turns and tooltool, n.: Any outside capability an AI model is allowed to use — a web search, a calculator, a code runner, a database or an app — described to the model so it can ask for it by name. results, serialized by a chat templatechat template, n.: The exact text layout a chat AI was trained on for marking who is speaking — system, user or assistant. Software must reproduce it precisely, or the model performs noticeably worse.. Because a model's behavior is determined by its weightsweights, n.: The learned numbers inside a neural network that set how strongly each part influences the next. The finished set of weights is, in effect, the trained model itself. together with its input, the prompt is the main way users and developers steer it without training, and the basis of prompt engineeringprompt engineering, n.: The skill of writing requests to an AI — picking the words, examples, format and background information it receives — so that it reliably gives the result you want, without retraining it. and in-context learningin-context learning, n.: A language model's ability to pick up a new task from instructions or examples placed in its input, on the spot, without any retraining — like a quick study learning a game's rules from one round.. Its length is limited by the context windowcontext window, n.: How much an AI model can read and keep in view at once: the instructions, the conversation so far and any attached documents. Text that does not fit has to be cut, summarized or left out..
Put the style guide in the prompt and the model will follow it.
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Generative media
The text description given to a text-to-image modeltext-to-image model, n.: An AI system that creates a picture from a written description, such as 'a fox reading a newspaper, in watercolor', usually within seconds., text-to-video modeltext-to-video model, n.: An AI that creates a short video clip — increasingly with matching sound — from a written description, such as 'a paper boat sailing down a rainy street'. or music generator to specify the desired output, often listing subject, style, composition and lighting, and sometimes paired with a negative promptnegative prompt, n.: A second prompt listing what you do not want in a generated image — 'blurry, extra fingers, watermark' — which the generator then steers away from. naming what to avoid.
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Computing
A symbol or message, such as a shell's '$' or 'C:\>', by which an interactive program signals that it is waiting for input from the user. This is the older computing sense, from command-line interfaces.
Origin
From the theater, where a prompter supplies actors' forgotten lines; command-line interfaces later used 'prompt' for the cue awaiting input. Text-generation research was conditioning models on prompts by 2018 — Angela Fan and colleagues' story generator worked from Reddit writing prompts — and GPT-2GPT-2, prop. n.: A 2019 OpenAI language model that wrote surprisingly fluent paragraphs. OpenAI at first withheld the full version, saying it could be misused for fake news, which made it famous. (2019) and GPT-3GPT-3, prop. n.: A huge 2020 OpenAI language model that could perform new tasks simply from instructions or a few examples written into its prompt, without retraining. It made prompting a new way of programming. (2020) made prompting the main way of specifying tasks to language models.
Usage note
The prompt is everything the model receives, not only what the user types; in deployed products most of it is hidden. A prompt does not reprogram a model: its effects are probabilistic and can be overridden by training or by adversarial input (see prompt injectionprompt injection, n.: Tricking an AI system by slipping in text that it mistakes for instructions — for example, a hidden line in an email telling an AI assistant to forward the user's messages to a stranger.).
Related
- prompt engineeringprompt engineering, n.: The skill of writing requests to an AI — picking the words, examples, format and background information it receives — so that it reliably gives the result you want, without retraining it.
- system promptsystem prompt, n.: Background instructions given to a chatbot before the conversation starts, setting its role, rules, tone and tools. Users usually never see it, but it shapes every reply.
- context windowcontext window, n.: How much an AI model can read and keep in view at once: the instructions, the conversation so far and any attached documents. Text that does not fit has to be cut, summarized or left out.
- in-context learningin-context learning, n.: A language model's ability to pick up a new task from instructions or examples placed in its input, on the spot, without any retraining — like a quick study learning a game's rules from one round.
- prompt templateprompt template, n.: A reusable prompt with blanks to fill in — for example, 'Summarize this email for a busy manager: {email}' — so a program can send the same tested instructions with different content each time.
- few-shot promptingfew-shot prompting, n.: Showing an AI a handful of examples of a task — inputs with the answers you want — inside the prompt, so it can copy the pattern for a new case.
- context engineeringcontext engineering, n.: Managing an AI model's reading material: choosing, trimming and arranging the instructions, documents, tool results and memories it is given on every turn, so that it has what the task needs and little else.
- prompt injectionprompt injection, n.: Tricking an AI system by slipping in text that it mistakes for instructions — for example, a hidden line in an email telling an AI assistant to forward the user's messages to a stranger.
- completioncompletion, n.: The text an AI language model writes in response to a prompt — literally, its continuation of the text it was given.
Sources
- Brown, T. B. et al. (2020). Language Models are Few-Shot Learners. NeurIPS 2020. arxiv.org/abs/2005.14165v2
- Fan, A., Lewis, M. & Dauphin, Y. (2018). Hierarchical Neural Story Generation. ACL 2018.
Mentioned in
AI artAI art, n.: Images, music or other artworks made with the help of AI — from early computer drawing programs to today's prompt-based image generators — and the debate over whether and how such work counts as art., chain of thoughtchain of thought, n.: Getting an AI to write out its reasoning step by step before giving an answer, the way a student shows their working. This often makes answers to math and logic problems more accurate., generative AIgenerative AI, n.: AI that produces something new on request, such as an essay, a picture, a song or a piece of code, having absorbed from enormous numbers of existing examples what such things typically look like., large language modellarge language model, n.: A computer program trained on enormous amounts of text to predict the words that come next. At vast scale, that simple skill lets it chat, answer questions, translate, summarize and write code., MidjourneyMidjourney, prop. n.: A popular paid AI image generator, first used by typing prompts into the chat app Discord, known for its polished, painterly look; it can also animate images into short videos., prompt engineeringprompt engineering, n.: The skill of writing requests to an AI — picking the words, examples, format and background information it receives — so that it reliably gives the result you want, without retraining it., text-to-image modeltext-to-image model, n.: An AI system that creates a picture from a written description, such as 'a fox reading a newspaper, in watercolor', usually within seconds., base modelbase model, n.: A language model fresh from its first broad training on text, before being taught to follow instructions or chat. It continues whatever text it is given instead of answering., completioncompletion, n.: The text an AI language model writes in response to a prompt — literally, its continuation of the text it was given., few-shot promptingfew-shot prompting, n.: Showing an AI a handful of examples of a task — inputs with the answers you want — inside the prompt, so it can copy the pattern for a new case., foundation modelfoundation model, n.: A very large AI model, built once at great cost from vast amounts of text, images or code gathered mostly from the internet, that many products then reuse as their starting point, from chatbots to coding tools., in-context learningin-context learning, n.: A language model's ability to pick up a new task from instructions or examples placed in its input, on the spot, without any retraining — like a quick study learning a game's rules from one round., negative promptnegative prompt, n.: A second prompt listing what you do not want in a generated image — 'blurry, extra fingers, watermark' — which the generator then steers away from., one-shotone-shot, v.: Slang for an AI completing a whole task correctly on the first try from a single request — 'it one-shotted the app' — with no corrections or follow-up needed., prompt engineerprompt engineer, n.: A person whose job is writing and testing the instructions given to AI models so they behave reliably; hyped in 2023 as a six-figure job anyone could learn, the role has since largely merged into ordinary engineering., prompt sensitivityprompt sensitivity, n.: The way an AI's answers, and its test scores, can change a lot after small changes to a prompt that should not matter — a different word, an extra space or examples in another order., prompt templateprompt template, n.: A reusable prompt with blanks to fill in — for example, 'Summarize this email for a busy manager: {email}' — so a program can send the same tested instructions with different content each time., Software 3.0Software 3.0, n.: Andrej Karpathy's name for programming computers by writing plain-language instructions for AI models, following traditional code (Software 1.0) and trained neural networks (Software 2.0)., system promptsystem prompt, n.: Background instructions given to a chatbot before the conversation starts, setting its role, rules, tone and tools. Users usually never see it, but it shapes every reply., wrapperwrapper, n.: A product built mostly on top of someone else's AI model, adding a simple interface or a few instructions; often used as a put-down implying the product is easy to copy., zero-shot learningzero-shot learning, n.: Doing a task without being shown a single example of it — say, an AI sorting reviews into positive and negative just from being told to, or recognizing an animal it never saw in training., English is the hottest new programming languageEnglish is the hottest new programming language, phr.: A catchphrase meaning that people can now get computers to do things by describing what they want to an AI in ordinary language, instead of writing code., parametric knowledgeparametric knowledge, n.: What an AI model 'knows' from training, stored in the numbers of its network rather than looked up in documents — like facts you have memorized rather than ones you check in a book., reversal cursereversal curse, n.: A quirk of language models: having learned that 'A is B', they often cannot answer the reverse question — they may know who a celebrity's mother is yet fail to say who her son is.