Within Few shot prompts
How to pick examples that actually help
Good few-shot examples clarify the task, format, labels, and edge cases without overloading the prompt with noise.
On this page
- What each example should teach the model
- Why relevance matters more than quantity
- A practical checklist for prompt examples
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Introduction
Few-shot prompting works because examples do more than illustrate a task: they show the model exactly how to behave. When a language model is given a handful of demonstrations, it infers temporary rules about what inputs look like, what outputs should contain, which labels are allowed, and how edge cases should be handled. The quality of those demonstrations often matters more than the length of the written instructions. Research and guidance from OpenAI, Anthropic, Google, and other practitioners consistently show that carefully chosen examples can significantly improve consistency, formatting, and task accuracy.[OpenAI Help Center+2Anthropic]help.openai.comBelow we present a number of prompt formats we find work…
The challenge is that not all examples are equally useful. A prompt packed with repetitive or poorly chosen demonstrations can confuse the model or even reduce performance. The goal is not to provide many examples, but to provide examples that teach the right temporary rules.[arXiv]arxiv.orgarXiv The Few-shot Dilemma: Over-prompting Large Language ModelsThe Few-shot Dilemma: Over-prompting Large Language ModelsSeptember 16, 2025…
What each example should teach the model
A useful few-shot example should communicate at least one important aspect of the task. Rather than treating examples as redundant demonstrations, it helps to think of each one as teaching a specific lesson.
For instance, in a sentiment-classification prompt:
- One example might teach the difference between positive and negative labels.
- Another might demonstrate the required output format.
- A third might show how to handle ambiguous wording.
- A fourth might clarify a difficult edge case.
Together, these examples create a compact rulebook that the model can follow throughout the conversation. Anthropic recommends providing realistic examples of both inputs and ideal outputs, including challenging cases that clarify expectations.[Anthropic]anthropic.comprompt engineering for business performancePrompt engineering for business performanceFeb 29, 2024 — 2. Few-shot prompting. It's helpful to give Claude realistic and speci…
Consider two versions of a prompt:
Less useful examples[ai.google.dev]ai.google.devGoogle AI for DevelopersPrompt design strategies | Gemini APIApr 28, 2026 — We recommend to always include few-shot examples in your prompts…
- “I love this product.” → Positive
- “This product is amazing.” → Positive
- “Great product.” → Positive
These examples repeat the same lesson.
More useful examples[ai.google.dev]ai.google.devGoogle AI for DevelopersPrompt design strategies | Gemini APIApr 28, 2026 — We recommend to always include few-shot examples in your prompts…
- “I love this product.” → Positive
- “The package arrived damaged.” → Negative
- “The product works, but customer support never replied.” → Negative
- “It is acceptable for the price.” → Neutral
The second set teaches label boundaries, variation in wording, and how mixed opinions should be interpreted.
Why relevance matters more than quantity
A common mistake is assuming that more examples automatically produce better results. In practice, relevance is usually more important than volume.
If the task is classifying customer-service messages, examples should resemble customer-service messages. If the task is extracting information from invoices, examples should look like invoices. Models learn patterns from the context they see, so demonstrations that closely match the real task are more informative than a larger collection of unrelated examples. Google’s prompting guidance notes that clear examples can sometimes communicate the task so effectively that lengthy instructions become unnecessary.[Google AI for Developers]ai.google.devGoogle AI for DevelopersPrompt design strategies | Gemini APIApr 28, 2026 — We recommend to always include few-shot examples in your prompts…
This principle also applies to style and formatting. If the desired output is a JSON object, the examples should use valid JSON. If the desired output is a concise business summary, the examples should be concise business summaries. Models often imitate demonstrated structure more reliably than they follow abstract descriptions of structure.[Anthropic]anthropic.comeffective context engineering for ai agentsEffective context engineering for AI agents29 Sept 2025 — Providing examples, otherwise known as few-shot prompting, is a well k…
Evidence from prompt-engineering practice repeatedly shows that a small number of highly relevant examples can outperform a much larger collection of loosely related ones. Anthropic’s guidance emphasises relevant examples that match the actual use case, while Google recommends using examples to directly demonstrate the desired behaviour.[AI with Grant]aiwithgrant.comUse Examples (Multishot Prompting). How to use few-shot examples to dramatically improve Claude's accuracy, consistency, and output quali…
Cover the edge cases, not just the easy cases
The easiest examples are often the least valuable.
Most language models can already classify obviously positive reviews, recognise straightforward dates, or summarise simple text. What frequently causes errors are the borderline cases.
Useful few-shot examples often include:[ai.google.dev]ai.google.devGoogle AI for DevelopersPrompt design strategies | Gemini APIApr 28, 2026 — We recommend to always include few-shot examples in your prompts…
- Ambiguous inputs.
- Mixed sentiments.
- Unusual formatting.
- Rare categories.
- Inputs that are easy to misinterpret.
For example, suppose a task is labelling support tickets as “billing”, “technical”, or “account”.
Easy examples might show:[dev.to]dev.to5 prompt engineering patterns that actually work in production 4mcjDEV Community5 Prompt Engineering Patterns That Actually Work in…9 Mar 2026 — Use Few-Shot Examples to Lock In Format. System prompts…
- Password reset → Account
- Cannot log in → Account
More useful examples might include:
- “My card was charged twice after a failed login attempt.”
- “I cannot access premium features even though payment succeeded.”
These cases force the prompt to reveal how overlapping categories should be resolved.
Anthropic specifically recommends including challenging examples and edge cases because they help the model understand the exact boundaries of the task. Similar recommendations appear in prompt-engineering guides and practitioner resources that emphasise diversity over repetition.[Anthropic+2AI with Grant]anthropic.comprompt engineering for business performancePrompt engineering for business performanceFeb 29, 2024 — 2. Few-shot prompting. It's helpful to give Claude realistic and speci…
Avoid teaching accidental rules
Examples influence the model whether the pattern is intentional or not.
Suppose every positive example contains short sentences and every negative example contains long sentences. The model may incorrectly learn that sentence length predicts sentiment.
Likewise, if every example labelled “urgent” contains the word “immediately”, the model may begin relying on that keyword rather than learning the broader concept.
This problem is sometimes called a spurious pattern: the model notices a correlation in the examples that the prompt writer never intended.
To reduce this risk:
- Vary wording across examples.
- Use different sentence lengths.
- Include diverse vocabulary.
- Avoid repeating identical structures.
- Ensure labels depend on meaning rather than superficial clues.
The objective is to teach the genuine task rule, not an accidental shortcut.
Show the format, don’t just describe it
One of the strongest uses of few-shot prompting is format control.[datacamp.com]datacamp.comfew shot promptingFew-Shot Prompting: Examples, Theory, Use Cases21 Jul 2024 — Few-shot prompting is a technique that involves providing a language model w…
A model may misunderstand a written instruction such as:
Return the answer as a JSON object with three fields.
But after seeing several correctly formatted examples, it often reproduces the structure with much greater consistency. OpenAI, Anthropic, and Google all highlight examples as a powerful way to enforce output structure and style.[OpenAI Help Center+2Claude]help.openai.comBelow we present a number of prompt formats we find work…
For example:
Input:
“Meeting moved to Friday.”
Output:
{
“event”: “meeting”,
“change”: “rescheduled”,
“date”: “Friday”
}
A few demonstrations like this teach:
- Field names.
- Ordering.
- Formatting conventions.
- Level of detail.
- Expected output length.
In many practical applications, examples are used primarily to define format rather than task logic.
A practical checklist for prompt examples
Before adding an example to a few-shot prompt, ask what it contributes.[cloud.google.com]cloud.google.comwhat is prompt engineeringGoogle CloudPrompt Engineering for AI GuideApr 5, 2026 — Use Few-Shot Prompting: Tactic. Prompt Example. Provide a few examples of desire…
A strong set of examples usually satisfies most of the following criteria:
- Representative: resembles the real inputs the model will receive.
- Diverse: covers different wording styles and scenarios.
- Non-redundant: teaches something new rather than repeating an earlier example.
- Correctly labelled: contains no mistakes or ambiguities in the target output.
- Format-consistent: demonstrates exactly how outputs should appear.
- Edge-case aware: includes at least some difficult or borderline situations.
- Compact: contains enough information to teach the pattern without unnecessary noise.
Practitioners often find that two to five carefully selected examples are sufficient for many tasks, especially when each example teaches a distinct lesson.[AI with Grant+2Udemy Blog]aiwithgrant.comUse Examples (Multishot Prompting). How to use few-shot examples to dramatically improve Claude's accuracy, consistency, and output quali…
Why more examples can sometimes hurt
It is tempting to keep adding examples whenever performance is imperfect. However, recent research suggests that excessive demonstrations can sometimes reduce effectiveness rather than improve it. Researchers studying what they call the “few-shot dilemma” found cases where adding more examples eventually degraded performance, especially when examples became repetitive or overloaded the context with unnecessary information.[arXiv]arxiv.orgarXiv The Few-shot Dilemma: Over-prompting Large Language ModelsThe Few-shot Dilemma: Over-prompting Large Language ModelsSeptember 16, 2025…
This finding reinforces a practical rule: optimise example quality before increasing example quantity.
A concise prompt containing four carefully chosen demonstrations often works better than a prompt containing twenty examples that repeat the same pattern. The purpose of few-shot prompting is not to overwhelm the model with data but to provide the clearest possible demonstrations of the temporary rules you want it to follow.[arXiv+2Claude]arxiv.orgarXiv The Few-shot Dilemma: Over-prompting Large Language ModelsThe Few-shot Dilemma: Over-prompting Large Language ModelsSeptember 16, 2025…
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Endnotes
1.
Source: help.openai.com
Link:https://help.openai.com/en/articles/6654000-best-practices-for-prompt-engineering-with-openai-api
Source snippet
Below we present a number of prompt formats we find work...
2.
Source: anthropic.com
Title: prompt engineering for business performance
Link:https://www.anthropic.com/news/prompt-engineering-for-business-performance
Source snippet
Prompt engineering for business performanceFeb 29, 2024 — 2. Few-shot prompting. It's helpful to give Claude realistic and speci...
3.
Source: ai.google.dev
Link:https://ai.google.dev/gemini-api/docs/prompting-strategies
Source snippet
Google AI for DevelopersPrompt design strategies | Gemini APIApr 28, 2026 — We recommend to always include few-shot examples in your prompts...
4.
Source: arxiv.org
Title: arXiv The Few-shot Dilemma: Over-prompting Large Language Models
Link:https://arxiv.org/abs/2509.13196
Source snippet
The Few-shot Dilemma: Over-prompting Large Language ModelsSeptember 16, 2025...
Published: September 16, 2025
5.
Source: claude.com
Title: Best practices for prompt engineering
Link:https://claude.com/blog/best-practices-for-prompt-engineering
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Best practices for prompt engineering - ClaudeNov 10, 2025 — Pro tip: Start with one example (one-shot). Only add more examples (fe...
6.
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Title: Prompting best practices
Link:https://platform.claude.com/docs/en/build-with-claude/prompt-engineering/claude-prompting-best-practices
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best practices - Claude API DocsComprehensive guide to prompt engineering techniques for Claude's latest models, covering clarity, exampl...
7.
Source: cloud.google.com
Title: what is prompt engineering
Link:https://cloud.google.com/discover/what-is-prompt-engineering
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Google CloudPrompt Engineering for AI GuideApr 5, 2026 — Use Few-Shot Prompting: Tactic. Prompt Example. Provide a few examples of desire...
8.
Source: anthropic.com
Title: effective context engineering for ai agents
Link:https://www.anthropic.com/engineering/effective-context-engineering-for-ai-agents
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Effective context engineering for AI agents29 Sept 2025 — Providing examples, otherwise known as few-shot prompting, is a well k...
9.
Source: blog.udemy.com
Title: few shot learning
Link:https://blog.udemy.com/few-shot-learning/
Source snippet
Udemy BlogFew-Shot Learning: Explained with Code Examples12 Dec 2025 — Few-shot prompting means seeding your prompt with 2–5 crisp, worke...
10.
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Link:https://www.google.com/
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11.
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Link:https://about.google/
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Our products, technology and company...Learn more about Google. Explore our innovative AI products and services, and how we're using tec...
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14.
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DEV Community5 Prompt Engineering Patterns That Actually Work in...9 Mar 2026 — Use Few-Shot Examples to Lock In Format. System prompts...
16.
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Link:https://www.aiwithgrant.com/guides/anthropic-prompt-engineering-overview
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17.
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Link:https://smithery.ai/skills/jamesrochabrun/anthropic-prompt-engineer
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SkillFew-Shot Examples. Provide 2-5 diverse examples showing the pattern you want. 6. Role Assignment. Give Claude a specific role or per...
18.
Source: datacamp.com
Title: few shot prompting
Link:https://www.datacamp.com/tutorial/few-shot-prompting
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Few-Shot Prompting: Examples, Theory, Use Cases21 Jul 2024 — Few-shot prompting is a technique that involves providing a language model w...
19.
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Additional References
22.
Source: medium.com
Link:https://medium.com/%40kiranvutukuri/85-few-shot-prompting-learning-from-examples-8fe9301054eb
Source snippet
85. Few-Shot Prompting: Learning from ExamplesFew-shot prompting is one of the most powerful techniques in modern LLM usage. Instead of e...
23.
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Link:https://github.com/NirDiamant/Prompt_Engineering/blob/main/all_prompt_engineering_techniques/few-shot-learning.ipynb
Source snippet
Few-Shot Learning and [In-Context Learning]({{ 'in-context-learning/' | relative_url }}) TutorialEdge cases: Include examples of unusual or difficult cases. Prompt Engineering: Clear i...
24.
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Link:https://www.matillion.com/blog/gen-[ai-prompt
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25.
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Link:https://www.linkedin.com/pulse/top-prompting-strategies-unveiled-anthropic-experts-martin-treiber-qge0f
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Top Prompting Strategies Unveiled by Anthropic ExpertsIn multi-shot prompting, the model is provided with multiple examples of the task i...
26.
Source: youtube.com
Link:https://www.youtube.com/watch?v=ggXckjI_w-w
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Few-Shot PromptingFew-Shot prompting enables a language model to perform tasks more effectively by providing a few examples within the pr...
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30.
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Everything you need to know about few shot prompting - RedditApril 30, 2024 — We put together a 3,000 word guide on everything related to...
Published: April 30, 2024
31.
Source: medium.com
Title: Prompt Engineering via Prompt Patterns— Few Shots Prompting
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Source snippet
June 5, 2024 — Few shot examples are kind of a script to generate further content with and ChatGPT follows the script brilliantly filling...
Published: June 5, 2024
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