# Few-shot learning / few-shot prompting

Record: term-few-shot-learning · Type: term · Edition: 0.20.0 · Evidence cutoff: 2026-09-15

[Read in the atlas](https://theaiatlas.org/ideas/few-shot-learning/) · [Complete evidence](https://theaiatlas.org/evidence.html#idea-few-shot-learning) · [JSON](https://theaiatlas.org/records/term-few-shot-learning.json) · [Pinned complete dataset](https://theaiatlas.org/editions/e74392d479c0e7da8636a7d6a0454d03510df86ffca9931eb86babd952665ca5/data.json)

Dataset pointer: `/glossary/110`. Reviewed: 2026-09-15.

> This is a curated, AI-assisted editorial atlas, not a census, affiliation classifier or independently fact-checked authority.

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## Publication dates and source age

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

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 establish validity or a new source-reading date. Unknown dates and month/year precision remain explicit in the JSON record.

## /summary

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

Claim: claim-term-few-shot-learning-1a5192ba3d7825ca26b24a72. Annotation: synthesis.

[google-llm-tuning](https://developers.google.com/machine-learning/crash-course/llm/tuning)

## /definition

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.

Claim: claim-term-few-shot-learning-1e4c26398ee834b2e16dc7b8. Annotation: synthesis.

[google-llm-tuning](https://developers.google.com/machine-learning/crash-course/llm/tuning)

## /placement

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

Claim: claim-term-few-shot-learning-25f52a21ad9f2e54a62ed1b2. Annotation: editorial.

[google-llm-tuning](https://developers.google.com/machine-learning/crash-course/llm/tuning)

## /distinction

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.

Claim: claim-term-few-shot-learning-09422ce4d5c74cb753a9bb98. Annotation: synthesis.

[glossary-eval-gpt3](https://arxiv.org/html/2005.14165v4)

## Source provenance

### google-llm-tuning

[LLMs: Fine-tuning, distillation, and prompt engineering](https://developers.google.com/machine-learning/crash-course/llm/tuning)

Google for Developers · First-hand source (primary) · Published: undated · Updated: 2025-12-03 · Material last read: 2026-09-15 · Verification: read

Read scope is described in the source note.

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.

No archive check recorded.

### glossary-eval-gpt3

[Language Models are Few-Shot Learners](https://arxiv.org/html/2005.14165v4)

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

Read scope is described in the source note.

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.

No archive check recorded.