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one-shot

verbAlso: one-shotted, one-shotting, one-shot it

In plain wordsSlang 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.

  1. 1

    AI use

    Of an AI model or agent: to produce a complete, working result from a single promptprompt, n.: The 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., without iteration, follow-up instructions or human fixes ('it one-shotted the migration'). The verb borrows the vocabulary of few-shotfew-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. and zero-shotzero-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. methods but means something different: those terms count the worked examples supplied in a prompt, whereas the slang counts attempts. One-shot demos — a game, website or tool built from one request — became a staple of model launches and social media in 2024–2026; skeptics note that they showcase best cases and hide failed attempts.

    The new model one-shotted the whole dashboard, tests included.

  2. 2

    General slang

    To overwhelm or permanently change a person through a single experience: used, often jokingly, of people whose worldview or behavior was transformed after one intense encounter with a technology, idea or substance, including chatbot users who became fixated on an AI after one striking conversation.

Origin

From video-game slang, in which to one-shot an opponent is to defeat them with a single hit. The AI sense spread with coding assistants in 2024 and became ubiquitous with agentic coding tools in 2025; the sense of a person transformed by one experience circulated on social media in the same period.

Usage note

Not to be confused with one-shot prompting (supplying one worked example; see 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.) or with one-shot learning in machine learning, which means learning a category from a single labeled example.

Related

See also
  • vibe codingvibe coding, n.: Programming by conversation: you ask an AI for what you want, try out what it produces and ask again until it works, trusting the code without really reading it.
  • agentic codingagentic coding, n.: Writing software by directing AI agents that plan, edit and test code themselves, with the human acting more as a reviewer and manager than as the person typing every line.
  • coding agentcoding agent, n.: An AI agent that writes and fixes software by itself: it reads a codebase, edits files, runs commands and tests, and keeps working until a feature is built or a bug is fixed.
  • 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.
  • 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.
  • pelican on a bicyclepelican on a bicycle, n.: A joke test in which an AI model is asked to draw, by writing code, a pelican riding a bicycle; comparing the results became a popular, lighthearted way to judge each new model.
Contrast with
  • 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.

Mentioned in

vibe codingvibe coding, n.: Programming by conversation: you ask an AI for what you want, try out what it produces and ask again until it works, trusting the code without really reading it.