A field guide to AI positions

Few-shot learning / few-shot prompting

Prompting method.

Dated sources are over 18 months old; other dates are unknown.

Publication dates and source age

Sources counted: 2

Newest dated source: 2020-05-28

Assessed at this edition's evidence cutoff: 2026-09-15. 18-month boundary: 2025-03-15.

Some publication dates are unknown; the newest dated source may not be the newest source overall.

Publication age does not tell us whether a claim is still valid. Reading an old source again does not make its publication date newer. An update date does not establish that the passage we used was updated.

Open in the glossary Reading notes · Structured record

Prepared with AI assistance. Not independently fact-checked. Original sources and qualifications are below.

In plain language

Giving a model a few worked examples in the prompt to show the kind of response wanted. [1]

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Explanation based on the linked sources. Claim ID: claim-term-few-shot-learning-1a5192ba3d7825ca26b24a72. Edition 0.20.0.

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Limits & distinctions

Few examples in the prompt does not mean little prior training. Improvements vary by task and model; more examples do not guarantee a better result. [2]

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Explanation based on the linked sources. Claim ID: claim-term-few-shot-learning-09422ce4d5c74cb753a9bb98. Edition 0.20.0.

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A fuller explanation

In LLM prompting, the examples are part of the input, not a separate training run. For instance, show two sample messages labeled by topic before asking it to label a third. [1]

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Explanation based on the linked sources. Claim ID: claim-term-few-shot-learning-1e4c26398ee834b2e16dc7b8. Edition 0.20.0.

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How it relates to the map

Atlas reading question: were examples supplied when a model's result was reported? [1]

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Editorial interpretation or qualification. Claim ID: claim-term-few-shot-learning-25f52a21ad9f2e54a62ed1b2. Edition 0.20.0.

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https://theaiatlas.org/ideas/few-shot-learning/

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Sources and what we read

  1. 1. LLMs: Fine-tuning, distillation, and prompt engineering

    Google for Developers · First-hand source · Published date unavailable · Updated 2025-12-03 · Material last read 2026-09-15

    Publication dates and source age

    Sources counted: 1

    Publication dates are unavailable.

    Assessed at this edition's evidence cutoff: 2026-09-15. 18-month boundary: 2025-03-15.

    Some publication dates are unknown; the newest dated source may not be the newest source overall.

    Publication age does not tell us whether a claim is still valid. Reading an old source again does not make its publication date newer. An update date does not establish that the passage we used was updated.

    Read scope is described in the source note.

    No archive check recorded.

    Read fine-tuning and prompt engineering, including the distinction between parameter updates and examples supplied as input. Used to distinguish these processes, without adopting general claims that fine-tuning is always necessary or improves every task.

    Source ID: google-llm-tuning

  2. 2. Language Models are Few-Shot Learners

    Tom B. Brown and coauthors / arXiv · First-hand source · Published 2020-05-28 · Updated 2020-07-22 · Material last read 2026-09-15

    Source is over 18 months old.

    Publication dates and source age

    Sources counted: 1

    Newest dated source: 2020-05-28

    Assessed at this edition's evidence cutoff: 2026-09-15. 18-month boundary: 2025-03-15.

    Publication age does not tell us whether a claim is still valid. Reading an old source again does not make its publication date newer. An update date does not establish that the passage we used was updated.

    Read scope is described in the source note.

    No archive check recorded.

    Read the abstract, section 2 on zero-shot and few-shot settings, and section 4 on training-data overlap. Version 4 is dated 22 July 2020. The reported results concern GPT-3 and are not current model rankings; detected overlap did not uniformly inflate scores.

    Source ID: glossary-eval-gpt3

Edition and machine-readable evidence

Content version 0.20.0. Evidence cutoff 2026-09-15; this does not mean every source was read on that day.

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