Within Language Models
When examples act like temporary instructions
A few examples in a prompt can steer a model into classification, translation, or formatting without permanently retraining it.
On this page
- How examples define the expected continuation
- Why in context learning is not ordinary training
- When unclear examples make outputs drift
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Introduction
One of the most surprising features of modern language models is that they can often learn a new task from only a handful of examples placed directly in the prompt. Instead of retraining the model or changing its internal parameters, a user can provide a few demonstrations and the model will often continue the pattern as though it has been given a temporary rulebook. This technique is known as few-shot prompting and is a common form of in-context learning. [OpenAI Developers]developers.openai.comOpenAI DevelopersPrompt engineering | OpenAI APIFew-shot learning… Few-shot learning lets you steer a large language model toward a ne…
For people trying to understand artificial intelligence, few-shot prompting reveals an important distinction: not every new behaviour requires new training. A language model can be guided into classifying text, translating phrases, extracting information, or following a specific format simply by seeing examples of the desired input-output relationship in its current context. The model behaves as though it has learned a task, but that learning exists only within the active conversation. [Prompting Guide+2OpenAI Developers]promptingguide.aiPrompting GuideFew-Shot PromptingFeb 1, 2026 — Few-shot prompting can be used as a technique to enable in-context learning where we provi…
How examples define the expected continuation
Language models generate text by predicting what should come next. When a prompt contains several examples, those examples become part of the context used for prediction. The model does not merely read the instructions; it also observes patterns in the demonstrations and attempts to continue them consistently. [Prompting Guide]promptingguide.aiPrompting GuideFew-Shot PromptingFeb 1, 2026 — Few-shot prompting can be used as a technique to enable in-context learning where we provi…
Consider a sentiment-classification prompt:
- Input: “I loved the service.” → Output: Positive
- Input: “The product arrived broken.” → Output: Negative
- Input: “The website was confusing.” → Output:?
The examples implicitly establish several temporary rules:
- The task is sentiment classification. [launchdarkly.com]launchdarkly.comLaunch Darkly Prompt Engineering Best Practices: Tutorial & Examples Few-shotFor anything more complex, provide the AI with specific examples of what you want. Task: "Classify customer sentiment and provide confide…
- Only a small label set is allowed.
- The output should be a single category rather than a paragraph.
- The relationship between input and output follows the demonstrated pattern.
When the model reaches the third example, it often predicts the continuation that best fits those observed rules. OpenAI’s prompting guidance explicitly recommends few-shot examples because they help steer a model toward a new task and desired output format without additional training. [OpenAI Developers]developers.openai.comOpenAI DevelopersPrompt engineering | OpenAI APIFew-shot learning… Few-shot learning lets you steer a large language model toward a ne…
The same mechanism works for formatting. If several examples produce JSON objects, tables, or labelled fields, the model tends to continue using that structure. If examples use a particular writing style or tone, the model often adopts those characteristics as well. In effect, the examples serve as temporary policy instructions embedded in the context window. [Anthropic+2Claude]anthropic.comeffective context engineering for ai agentsEffective context engineering for AI agentsSep 29, 2025 — Providing examples, otherwise known as few-shot prompting, is a well k…
Why in-context learning is not ordinary training
Few-shot prompting can look like learning, but it differs fundamentally from model training.
During training, a model’s parameters are updated through optimisation across large datasets. The changes become part of the model’s long-term behaviour and remain available in future conversations. During few-shot prompting, no parameter updates occur. The model simply uses the examples currently visible in its context to influence the next-token predictions it generates. [arXiv]arxiv.orgarXiv Structured Prompting: Scaling In-Context Learning to 1,000 ExamplesStructured Prompting: Scaling In-Context Learning to 1,000 ExamplesDecember 13, 2022…
Researchers often refer to this behaviour as in-context learning: the model adapts to information supplied within the prompt itself. Once the conversation ends or the examples are removed, the temporary adaptation disappears. [Prompthub]prompthub.usin context learning guideIn Context Learning GuideOct 23, 2025 — Few-shot prompting is just one example of in-context learning, as are zero-shot and one…
A useful analogy is a temporary briefing document. Imagine giving a human assistant three examples of how customer complaints should be categorised. The assistant may follow the demonstrated scheme for the rest of the session without permanently changing how they classify information in future jobs. Few-shot prompting works in a similar way. The examples influence behaviour within the current context but do not rewrite the underlying model. [IBM]ibm.comWhat is few shot prompting?Few-shot prompting refers to the process of providing an AI model with a few examples of a task to guide it…
Research has shown that language models can often infer the underlying task from examples alone. In some cases, models can even generate an explicit instruction describing the task after seeing only a few demonstrations, suggesting that they are extracting a temporary rule from the examples rather than merely copying surface patterns. [arXiv]arxiv.orgInstruction Induction: From Few Examples to Natural Language Task DescriptionsMay 22, 2022…
Why a few examples can be surprisingly powerful
The effectiveness of few-shot prompting depends on the model’s pre-existing knowledge. The examples do not teach language from scratch. Instead, they activate capabilities already present within the model and indicate which capability should be used. [Prompting Guide]promptingguide.aiPrompting GuideFew-Shot PromptingFeb 1, 2026 — Few-shot prompting can be used as a technique to enable in-context learning where we provi…
For example, a large language model may already possess knowledge about:
- Translation between languages.
- Sentiment analysis.
- Information extraction.
- Question answering.
- Structured formatting.
A few examples help the model determine which of these behaviours is currently expected and what form the answer should take. This is why two or three demonstrations can sometimes achieve results that would previously have required a dedicated machine-learning model for a specific task. [OpenAI Developers+2Anthropic]developers.openai.comOpenAI DevelopersPrompt engineering | OpenAI APIFew-shot learning… Few-shot learning lets you steer a large language model toward a ne…
Anthropic and OpenAI both recommend examples as a practical way to improve consistency and accuracy, especially when the desired behaviour involves specialised formatting or edge cases that may not be obvious from instructions alone. [Anthropic+2Anthropic]anthropic.comprompt engineering for business performancePrompt engineering for business performance29 Feb 2024 — 2. Few-shot prompting. It's helpful to give Claude realistic and specif…
When unclear examples make outputs drift
Few-shot prompting is powerful, but it is also fragile. The temporary rules inferred by the model depend heavily on the quality of the examples provided.
If examples are inconsistent, the model may struggle to determine the intended pattern. For instance, a prompt that alternates between long explanations and short labels sends mixed signals about the desired output format. Likewise, examples that use overlapping categories can make classification tasks less reliable. [LaunchDarkly]launchdarkly.comLaunch Darkly Prompt Engineering Best Practices: Tutorial & Examples Few-shotFor anything more complex, provide the AI with specific examples of what you want. Task: "Classify customer sentiment and provide confide…
Researchers have found that performance can vary depending on factors such as:
- Which examples are selected.
- The order in which examples appear.
- The wording of the prompt template.
- The balance of positive and negative cases.
- Whether examples cover important edge cases. [arXiv]arxiv.orgTowards Informative Few-Shot Prompt with Maximum Information Gain for In-Context LearningOctober 13, 2023…
A common failure mode is drift. The model initially follows the demonstrated pattern but gradually deviates when the examples are ambiguous or incomplete. For example, if all demonstrations are simple cases, the model may produce inconsistent outputs when faced with more complicated inputs. Anthropic specifically recommends including realistic and challenging examples to make the intended behaviour clearer. [Anthropic]anthropic.comprompt engineering for business performancePrompt engineering for business performance29 Feb 2024 — 2. Few-shot prompting. It's helpful to give Claude realistic and specif…
More examples are not always better. Recent research has identified situations in which excessive or poorly chosen demonstrations can reduce performance rather than improve it, a phenomenon sometimes described as an “over-prompting” effect. The usefulness of examples depends on their relevance and information value, not merely their quantity. [arXiv]arxiv.orgThe Few-shot Dilemma: Over-prompting Large Language…16 Sept 2025 — In the experiments, we discover a few-shot dilemma caused by o…
What few-shot prompts reveal about modern AI
Few-shot prompting demonstrates that language models are not limited to executing fixed commands. They can infer temporary task rules from examples, apply those rules to new inputs, and maintain the pattern throughout a conversation. Yet this adaptation occurs entirely within the prompt context rather than through permanent retraining. [Prompting Guide+2OpenAI Developers]promptingguide.aiPrompting GuideFew-Shot PromptingFeb 1, 2026 — Few-shot prompting can be used as a technique to enable in-context learning where we provi…
Understanding this distinction helps explain why modern AI systems can appear remarkably flexible. A handful of demonstrations can temporarily transform a general-purpose language model into a classifier, translator, formatter, editor, or data extractor. The model has not become a new system; it has simply been guided by examples that define what continuation is expected next. [IBM+2Comet]ibm.comWhat is few shot prompting?Few-shot prompting refers to the process of providing an AI model with a few examples of a task to guide it…
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Endnotes
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Source: developers.openai.com
Link: https://developers.openai.com/api/docs/guides/prompt-engineeringSource snippet
OpenAI DevelopersPrompt engineering | OpenAI APIFew-shot learning... Few-shot learning lets you steer a large language model toward a ne...
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Source: ibm.com
Link: https://www.ibm.com/think/topics/few-shot-promptingSource snippet
What is few shot prompting?Few-shot prompting refers to the process of providing an AI model with a few examples of a task to guide it...
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Source: prompthub.us
Title: in context learning guide
Link: https://www.prompthub.us/blog/in-context-learning-guideSource snippet
In Context Learning GuideOct 23, 2025 — Few-shot prompting is just one example of in-context learning, as are zero-shot and one...
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Source: help.openai.com
Link: https://help.openai.com/en/articles/6654000-best-practices-for-prompt-engineering-with-openai-apiSource snippet
✓ Few-shot - provide a couple of examples. Extract...Read more...
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Source: anthropic.com
Title: effective context engineering for ai agents
Link: https://www.anthropic.com/engineering/effective-context-engineering-for-ai-agentsSource snippet
Effective context engineering for AI agentsSep 29, 2025 — Providing examples, otherwise known as few-shot prompting, is a well k...
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Source: platform.claude.com
Title: Prompting best practices
Link: https://platform.claude.com/docs/en/build-with-claude/prompt-engineering/claude-prompting-best-practicesSource snippet
best practices - Claude API DocsComprehensive guide to prompt engineering techniques for Claude's latest models, covering clarity, exampl...
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Source: arxiv.org
Title: arXiv Structured Prompting: Scaling In-Context Learning to 1,000 Examples
Link: https://arxiv.org/abs/2212.06713Source snippet
Structured Prompting: Scaling In-Context Learning to 1,000 ExamplesDecember 13, 2022...
Published: December 13, 2022
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Source: arxiv.org
Title: arXiv In-Context Principle Learning from Mistakes
Link: https://arxiv.org/abs/2402.05403 -
Source: arxiv.org
Link: https://arxiv.org/abs/2205.10782Source snippet
Instruction Induction: From Few Examples to Natural Language Task DescriptionsMay 22, 2022...
Published: May 22, 2022
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Source: anthropic.com
Title: prompt engineering for [business]({{ ‘business-adoption/’ | relative_url }}) performance
Link: https://www.anthropic.com/news/prompt-engineering-for-business-performanceSource snippet
Prompt engineering for business performance29 Feb 2024 — 2. Few-shot prompting. It's helpful to give Claude realistic and specif...
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Source: launchdarkly.com
Title: Launch Darkly Prompt Engineering Best Practices: Tutorial & Examples Few-shot
Link: https://launchdarkly.com/blog/prompt-engineering-best-practices/Source snippet
For anything more complex, provide the AI with specific examples of what you want. Task: "Classify customer sentiment and provide confide...
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Source: arxiv.org
Link: https://arxiv.org/abs/2310.08923Source snippet
Towards Informative Few-Shot Prompt with Maximum Information Gain for In-Context LearningOctober 13, 2023...
Published: October 13, 2023
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Source: arxiv.org
Link: https://arxiv.org/html/2509.13196v1Source snippet
The Few-shot Dilemma: Over-prompting Large Language...16 Sept 2025 — In the experiments, we discover a few-shot dilemma caused by o...
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Link: https://www.comet.com/site/blog/few-shot-prompting/Source snippet
Few-Shot Prompting for Agentic Systems: Teaching by Example7 Mar 2026 — Few-shot prompting uses several examples to define a pattern...
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comOpenAI | Research & DeploymentWe believe our research will eventually lead to artificial general intelligence, a system that can solve...
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Title: how to design few shot prompt with api
Link: https://community.openai.com/t/how-to-design-few-shot-prompt-with-api/656727Source snippet
There you add the examples you want the model to refer to when...Read more...
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Source: community.openai.com
Title: few shot prompting with structured outputs
Link: https://community.openai.com/t/few-shot-prompting-with-structured-outputs/1045058Source snippet
openai.comFew-Shot Prompting with Structured Outputs6 Dec 2024 — I'm new to the OpenAI API, and wondering how to give few-shot examples w...
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Source: community.openai.com
Title: convert few shot example to api code
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few shot example to api code - Prompting19 Aug 2023 — Hello all I have made a little example of few shot sentiment analysis using the GUI...
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Title: prompting [long context]({{ ‘long-context-cost/’ | relative_url }})
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Prompt engineering for Claude's long context windowSep 23, 2023 — Our goal with this experiment is to evaluate techniques to maximize Cla...
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Title: the few shot prompting guide
Link: https://www.prompthub.us/blog/the-few-shot-prompting-guideSource snippet
23 Oct 2025 — This guide dives deep into everything related to few shot prompting (also known as few shot learning, or in-context learnin...
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NEW: Why AI In-Context Learning Works (Explained)...
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Prompting GuideFew-Shot PromptingFeb 1, 2026 — Few-shot prompting can be used as a technique to enable in-context learning where we provi...
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OpenAIOpenAI is an American artificial intelligence (AI) research organization headquartered in San Francisco, consisting of OpenAI Gr...
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Anthropic official prompting guides. Complete guide to building skills for Claude. Innovative uses of AI...Read more...
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OpenAIOpenAI's mission is to ensure that artificial general intelligence benefits all of humanity....more OpenAI's mission is to ensure...
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OpenAIOpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of...
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Anthropic Prompt Engineering Techniques For Better ResultsJun 12, 2025 — This blog takes you through Anthropic prompt engineering, with s...
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100 Claude AI Prompts I Actually Use — Free Templates (2026)8 Jan 2026 — Master Anthropic's Claude with expert prompting techniques, XML...
Additional References
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85. Few-Shot Prompting: Learning from ExamplesFew-shot prompting is one of the most powerful techniques in modern LLM usage. Instead of e...
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Link: https://www.reddit.com/r/PromptEngineering/comments/1cgzkdi/everything_you_need_to_know_about_few_shot/Source snippet
Everything you need to know about few shot promptingWe put together a 3,000 word guide on everything related to few shot prompting. We pu...
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Link: https://medium.com/%40akankshasinha247/few-shot-prompting-teaching-ai-with-just-a-few-examples-6819273fd6e2Source snippet
Few-Shot Prompting: Teaching AI With Just a Few ExamplesFew-shot prompting is one of the most practical and powerful prompt engineering t...
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Engineering — Zero-Shot, One-Shot, Few-Shot & In...Few-shot prompting is a technique where we provide a small number of examples (typica...
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Use Examples (Multishot Prompting). How to use few-shot examples to dramatically improve Claude's accuracy, consistency, and output quali...
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Few-Shot Prompting: Techniques, Examples, and Best...Apr 22, 2025 — Few-shot prompting is a technique that involves supplying a language...
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Title: zero shot vs few shot prompting a guide with examples
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Zero-Shot vs Few-Shot prompting: A Guide with ExamplesApr 3, 2026 — Few-shot prompting is a method where you use a few examples in your p...
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Title: leveraging dynamic few shot prompt with azure openai
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dynamic few-shot prompt with Azure OpenAI4 Sept 2024 — Few-shot prompt is a technique used in natural language processing (NLP) where a m...
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Title: Few Shot Prompting Improving AI Model Performance
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27 Nov 2025 — Few shot prompting provides 2-8 input-output examples before the actual task to demonstrate desired behavior patter...
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Few-Shot Prompting: Give AI Examples and Watch It Learn11 Mar 2026 — Few-shot prompting means giving AI examples before your actual request...
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