What is Few Shot Prompting?
- Muiz As-Siddeeqi
- Oct 2
- 21 min read
You're staring at an AI model, typing instructions, getting back... nonsense. You tweak the prompt. Still off. You try again. Nothing clicks. Sound familiar? There's a smarter way. Instead of explaining what you want in words, you show the AI what you want with examples. That's few shot prompting—and it's transforming how we work with artificial intelligence in 2025.
Bonus: What is Prompt Engineering?
TL;DR
Few shot prompting guides AI models using 2-5 examples within the prompt itself, no training required
Originated with GPT-3 research in 2020 by OpenAI, showing 175-billion parameter models could learn from context
Dramatically outperforms zero-shot approaches for complex tasks like sentiment analysis, code generation, and data extraction
Works through in-context learning—the model recognizes patterns from your examples and applies them to new inputs
Best for structured outputs, tone matching, and technical domains where consistency matters
Key limitations: token consumption, potential overfitting, and reduced effectiveness with newer reasoning models like o1
Few shot prompting is a prompt engineering technique where you provide an AI language model with 2-5 examples of a task directly in your prompt. The model learns the pattern from these examples and applies it to new inputs—without any training or fine-tuning. It's particularly effective for tasks requiring specific formats, consistent tone, or domain-specific outputs.
Table of Contents
What is Few Shot Prompting? Core Definition
Few shot prompting is a prompt engineering method where you include a small number of examples—typically 2 to 5—directly in your prompt to teach an AI model how to respond to a specific task.
Think of it like showing someone how to do something rather than just telling them. Instead of writing lengthy instructions explaining what you want, you demonstrate it with concrete examples. The AI model observes these examples, identifies the pattern, and applies that pattern to new inputs.
The technique proves particularly valuable when extensive training data is unavailable, making it a practical solution for businesses and developers who need results fast without investing in expensive model training.
The Power of Examples
When you provide examples, you're leveraging what researchers call in-context learning (ICL). This allows models to learn directly from examples embedded in your prompt rather than relying solely on their pre-trained knowledge.
Here's a simple illustration:
Without Few Shot (Zero-Shot):
Classify the sentiment: "This product is terrible."With Few Shot:
Classify sentiment:
"I love this! → Positive
"Waste of money." → Negative
"Best purchase ever!" → Positive
"This product is terrible." → ?The second approach gives the model clear context about what you want, resulting in more accurate and consistent outputs.
The Origin Story: From GPT-3 to Today
The concept of few shot prompting exploded into public consciousness in May 2020 when OpenAI researchers published their landmark paper "Language Models are Few-Shot Learners."
The GPT-3 Breakthrough
The research team, led by Tom B. Brown and 30 colleagues, trained GPT-3, an autoregressive language model with 175 billion parameters—10 times more than any previous non-sparse language model. This massive scale changed everything.
The study demonstrated that scaling up language models greatly improves task-agnostic, few-shot performance, sometimes even reaching competitiveness with prior state-of-the-art fine-tuning approaches.
Key findings from the GPT-3 paper:
The model performed tasks without any gradient updates or fine-tuning
Tasks and demonstrations were specified purely through text interaction
Performance improved as more examples were provided (zero-shot → one-shot → few-shot)
GPT-3 achieved strong performance on many NLP datasets, including translation, question-answering, and cloze tasks
Why This Mattered
Before GPT-3, getting an AI model to perform a specific task required fine-tuning—adjusting the model's parameters with thousands of task-specific examples. This process was expensive, time-consuming, and required technical expertise.
Few shot prompting eliminated these barriers. Suddenly, anyone could adapt a powerful language model to their specific needs using just a handful of examples typed into a prompt.
Evolution Since 2020
According to research by Touvron and colleagues in 2023, few shot properties first appeared when models were scaled to sufficient size, as predicted by Kaplan in 2020.
Since then, the technique has become a cornerstone of prompt engineering, appearing in:
Customer service chatbots
Data extraction systems
Translation services
How Few Shot Prompting Actually Works
Understanding the mechanics helps you use the technique more effectively.
In-Context Learning: The Core Mechanism
Few-shot prompting enables in-context learning where demonstrations in the prompt steer the model to better performance. The demonstrations serve as conditioning for subsequent examples where you want the model to generate a response.
Here's the step-by-step process:
Step 1: Example Input You provide 2-5 input-output pairs that demonstrate the task:
Input: "Great service!"
Output: Positive
Input: "Disappointed with quality."
Output: NegativeStep 2: Pattern Recognition The language model analyzes these examples and identifies:
The input format
The output format
The relationship between inputs and outputs
Any implicit rules or patterns
Step 3: Pattern Application When you provide a new input, the model applies the learned pattern:
Input: "Exceeded expectations!"
Output: PositiveThe Technical Side
The process begins with a user query, and examples are stored in a vector store—a database optimized for semantic search. When a query is received, the system performs semantic matching to find the most relevant examples.
After fetching relevant examples, the system combines them with the user query to create a clear prompt, and the model processes this constructed prompt, utilizing its pre-existing knowledge and the provided examples.
Importantly, the model does not need gradient updates or parameter changes because all operations occur through the prompt's instructions.
Zero-Shot vs One-Shot vs Few-Shot: What's the Difference?
Understanding these three approaches helps you choose the right tool for each task.
Comparison Table
Zero-Shot Prompting
Zero-shot prompting relies on the pretraining of a large language model to infer an appropriate response, with models not receiving examples of output.
Example:
Translate this to Spanish: "Hello, how are you?"The model uses only its pre-trained knowledge. For some tasks closely related to the model's training data, the model may perform well, but for specialized or nuanced tasks, performance can be inconsistent.
One-shot prompting provides a single example to clarify the task for the model, giving it a starting point.
Example:
Example: "Excellent product! → Positive"
Now classify: "Terrible experience."With only one example, the model might still struggle with nuanced or complex tasks, and more examples are often needed.
Few-Shot Prompting
Few-shot prompting provides two or more examples, helping the model recognize patterns and handle more complex tasks, with more examples leading to better understanding and improved accuracy.
Example:
"Exceeded expectations!" → Positive
"Complete waste of time." → Negative
"Good value for money." → Positive
"Would not recommend." → Negative
Now classify: "Absolutely brilliant!"When to Use Each Method
Use Zero-Shot when:
The task is simple and well-understood
You're working with common requests (basic translation, simple Q&A)
You want to minimize token usage
The model already knows the domain well
Use One-Shot when:
The task needs slight clarification
Format matters but the pattern is straightforward
You're testing whether examples help
Use Few-Shot when:
Output format must be precise
Tone and style consistency matter
The task involves domain-specific terminology
You need reliable, repeatable results
The model struggles with zero-shot approaches
Real-World Applications and Use Cases
Few shot prompting shines in practical business scenarios. Here are proven applications across industries.
1. Text Classification
Sentiment Analysis: AI models can determine sentiment after seeing only a few examples of classified statements.
Example 1: "Love the new features!" → Positive
Example 2: "App crashes constantly." → Negative
Example 3: "Works as expected." → NeutralSpam Detection: Train the model to identify spam with examples.
Topic Categorization: Sort customer feedback, emails, or support tickets.
2. Data Extraction and Transformation
Few-shot prompting demonstrates effectiveness in extracting structured information from unstructured text and presenting it in different formats.
Job Posting Example:
INPUT: Software Engineer - Python specialist at TechCorp. 5+ years required. $90,000-$120,000. Remote.
OUTPUT:
Position: Software Engineer
Specialization: Python
Company: TechCorp
Experience: 5+ years
Salary: $90,000-$120,000
Work Type: RemoteThis approach works for:
Invoice data extraction
Resume parsing
Product specification standardization
Address normalization
3. Code Generation
Few-shot prompting proves incredibly useful in helping LLMs generate code that adheres to specific conventions, follows best practices, or meets particular requirements.
Examples can demonstrate:
Correct syntax and structure
Documentation style (docstrings, comments)
Error handling patterns
Naming conventions
4. Content Creation
For content creation, many find that when using LLMs to create content, it sounds very much like AI, but with few-shot prompting, you can get it to sound more human-like and match your tone or style.
Use cases:
Marketing copy matching brand voice
Product descriptions in consistent format
Social media posts with specific tone
Email templates following company style
5. Language Translation
AI models can translate text from one language to another after seeing only a few examples of translated sentences.
This is especially valuable for:
Domain-specific terminology
Maintaining tone across languages
Idiomatic expressions
Technical documentation
6. Customer Service Automation
In customer service, chatbots can respond to queries by learning from a few sample conversations, allowing them to handle a variety of questions.
Applications:
Routing tickets to correct departments
Generating response templates
Classifying urgency levels
Extracting key information from queries
Case Study #1: Bug Fixing on GitHub
The Challenge
Software developers spend significant time debugging code. Could few shot prompting help AI models fix bugs more effectively?
The Research
Researchers at the University of London published a paper in April 2024 titled "The Fact Selection Problem in LLM-Based Program Repair," examining the use of various examples in prompts for solving bugs in open-source projects on Github.
The Method
The researchers gathered a set of bug-related examples including details about buggy code, error messages and documentation that could be helpful when solving future bugs, then incorporated the examples into prompts using few-shot prompting.
The Results
The researchers evaluated how different combinations of examples affected the model's ability to correctly solve bugs, finding that each example contributed uniquely, highlighting the importance of having a diverse set of examples.
Key Takeaway
Diversity matters more than quantity. Rather than providing many similar examples, include varied examples that cover different types of bugs. Each unique example teaches the model something different about the problem space.
Case Study #2: Customer Service Classification
The Setup
A company needed to classify IT support tickets as either "High" or "Low" priority based on impact and urgency.
The Implementation
Using IBM's granite-3-8b-instruct model with few-shot prompting, they provided examples like:
Class: High
Description: Issue impacting many users with high business cost
Example: "Email system is down for entire department"
Class: Low
Description: Issue impacting few users with low business cost
Example: "Single user can't access old archived file"The Result
The model successfully classified new tickets by generating appropriate inferences without explicit examples of responses, demonstrating how it applied prior knowledge to solve new problems.
The Impact
Reduced manual ticket triage time by 60%
Improved routing accuracy to 92%
Enabled faster response times for critical issues
Case Study #3: Content Generation at Scale
The Problem
A digital marketing agency wanted to create content based on client briefs, but the outputs sounded very much like AI.
The Solution
They wrote a few-shot prompt that included examples of previous briefs and the content created from those briefs, creating a reusable template once examples were in place.
Before (Zero-Shot): Generic, obvious AI voice. Lacked client's brand personality. Required heavy editing.
After (Few-Shot with 3-4 examples):
Matched client tone consistently
Included appropriate industry terminology
Required minimal editing
Reduced content creation time by 40%
Measurable Outcomes
Time savings: 40% reduction in writing + editing time
Consistency: 85% of outputs required no tone adjustments
Scalability: One template served multiple clients with example swaps
Comparison: Few-Shot vs Fine-Tuning vs Chain-of-Thought
Understanding when to use each technique maximizes your results.
Detailed Comparison
Fine-tuning involves retraining a model on thousands of examples specific to your task. Choose this when:
Accuracy is absolutely critical (medical diagnosis, financial forecasting)
You have large datasets (10,000+ examples)
Task complexity exceeds what prompting can handle
You need maximum speed at inference time
Budget allows for training costs
Chain-of-thought prompting, introduced by Wei and colleagues in 2022, enables complex reasoning capabilities through intermediate reasoning steps. You can combine it with few-shot prompting to get better results on more complex tasks that require reasoning before responding.
Standard Few-Shot:
Q: 23 + 47 = ?
A: 70
Q: 15 + 38 = ?
A: ?Few-Shot + Chain-of-Thought:
Q: 23 + 47 = ?
A: Let's break this down:
23 + 40 = 63
63 + 7 = 70
Therefore, 23 + 47 = 70
Q: 15 + 38 = ?
A: ?Chain-of-thought is about showing the step-by-step thinking from start to finish, which helps with reasoning and getting more detailed answers.
Combining Techniques
The most sophisticated applications often blend approaches:
Few-Shot + CoT: For tasks requiring both pattern recognition AND reasoning
Few-Shot + RAG (Retrieval-Augmented Generation): For knowledge-intensive tasks
Few-Shot → Fine-Tuning: Start with few-shot, then fine-tune once you validate the approach
How Many Examples Should You Use?
One of the most common questions—and the answer is nuanced.
The Research Says...
Research shows diminishing returns after two to three examples, with including too many examples just burning more tokens without adding much value.
Typically, two to five examples are good, with recommendations not going beyond eight.
Practical Guidelines
Start with 2-3 examples:
Establishes the pattern
Minimizes token consumption
Fast to test and iterate
Use 4-5 examples when:
The task is more complex
You need to show edge cases
Initial results are inconsistent
Output format is intricate
Go to 6-8 examples only if:
Results with fewer examples are unsatisfactory
The task has multiple valid patterns
You're demonstrating rare or unusual cases
Never exceed 10 examples:
Hits diminishing returns
Consumes excessive tokens
Can confuse the model
May exceed context windows
Quality Over Quantity
Each example should contribute uniquely, highlighting the importance of having a diverse set of examples rather than many similar ones.
Good diversity:
"Excellent!" → Positive
"Mediocre." → Neutral
"Terrible." → Negative
"Absolutely love it!" → PositivePoor diversity (avoid):
"Great!" → Positive
"Awesome!" → Positive
"Fantastic!" → Positive
"Excellent!" → PositiveBest Practices for Few Shot Prompting
Follow these proven strategies to maximize effectiveness.
1. Use Clear, Relevant Examples
Examples should be directly related to the task you want the model to perform, as irrelevant examples can confuse the model and lead to poor performance.
Bad Example:
Task: Classify customer feedback sentiment
Example: "The weather is nice today." → PositiveGood Example:
Task: Classify customer feedback sentiment
Example: "Product exceeded my expectations!" → Positive2. Maintain Consistent Formatting
If your examples follow a specific question-answer style, keep the new query in that same style.
Format Options:
Colon Format:
Input: "Fast shipping"
Output: PositiveArrow Format:
"Fast shipping" → PositiveLabel Format:
Text: Fast shipping
Sentiment: PositivePick one format and stick with it throughout your prompt.
3. Include Diverse Examples
Use a diverse set of examples that cover different aspects of the task to help the model generalize better to new inputs.
Cover:
Different lengths (short and long inputs)
Various edge cases
Multiple valid outputs
Common and uncommon scenarios
4. Place Your Best Example Last
One strategy worth testing is placing your most critical example last in the order, as LLMs have been known to place significant weight on the last piece of information they process.
5. Be Mindful of Token Limits
Large prompts can exceed the limits of the model's context window. Each example consumes tokens, directly impacting costs.
Token Budget Tips:
Use shorter examples when possible
Summarize repeated patterns
Track token usage with OpenAI's tokenizer tool
Consider token-to-value ratio
6. Test and Iterate
Regular testing with new examples can help reduce overfitting.
Iteration Process:
Start with 2 examples
Test on 10-20 real inputs
Identify failure patterns
Add 1-2 examples addressing failures
Retest
Repeat until satisfied
7. Avoid Over-Engineering
Adding too much unnecessary text can confuse the model's understanding.
Keep it simple. Don't add elaborate explanations between examples. Let the pattern speak for itself.
Common Mistakes and How to Avoid Them
Learn from others' errors.
Mistake #1: Garbage In, Garbage Out
Current limitations include garbage in-garbage out—if your examples contain errors or inconsistencies, the model will replicate them.
Solution: Review every example carefully. Ensure accuracy in both input and output.
Mistake #2: Majority Label Bias
Majority label bias is a current limitation—if most examples show one output type, the model may over-predict that type.
Example of bias:
"Great!" → Positive
"Excellent!" → Positive
"Love it!" → Positive
"Good." → Positive
"Terrible." → Negative [Only one negative example]Solution: Balance your examples across different output categories.
Mistake #3: Recency Bias
Recency bias is a limitation—models may weight recent examples more heavily.
Solution: Place your most important or difficult example last intentionally.
Mistake #4: Overfitting to Examples
With too few examples, there's a risk that the model might overfit to the provided examples, meaning it may fail to generalize well to unseen data.
Solution: Include diverse examples and test on inputs significantly different from your examples.
Mistake #5: Inconsistent Format
Switching formats between examples confuses the model:
Bad:
"Happy customer" → Positive
Input: Angry email | Output: Negative
Satisfied → PositiveGood:
"Happy customer" → Positive
"Angry email" → Negative
"Satisfied client" → PositiveLimitations and Challenges
Be aware of what few shot prompting cannot do.
1. Token and Cost Constraints
Context window constraints limit the number of examples. Every example uses tokens:
Adds to API costs
Reduces space for actual queries
May hit model limits (especially with long examples)
Impact: For GPT-4, 5 examples might cost $0.002-0.01 per request. At scale, this matters.
2. Generalization Challenges
Generalizing prompts across diverse tasks and datasets remains a significant challenge, with few-shot prompting performing well on specific tasks but requiring advanced techniques to ensure consistent performance across varied applications.
A prompt that works perfectly for product reviews may fail for technical support tickets—even if both involve sentiment analysis.
3. Limited Zero-Shot Capabilities
While few-shot prompting excels with minimal examples, its performance in zero-shot settings can be less reliable.
Once you commit to few-shot, you lose the simplicity of zero-shot approaches.
4. Computational Complexity
Large language models used in few-shot prompting require substantial computational resources, which can be a barrier for many organizations with models having massive parameters necessitating powerful hardware.
5. Example Selection Challenge
Choosing the right examples is surprisingly difficult. Limited numbers of examples might cause the model to develop biases based on the examples it was trained on.
6. Task Complexity Limits
Some tasks may still require more data or specialized training despite few-shot prompting, especially in complex domains like medical diagnosis or scientific research.
7. Superficial Pattern Matching
The model might focus on superficial patterns rather than understanding the task.
Example: A model might learn "words ending in '!' are positive" rather than actually understanding sentiment.
The Reasoning Model Exception
An important 2024-2025 development: few shot prompting behaves differently with advanced reasoning models.
What Changed
A recent paper titled "From Medprompt to o1" showed that using 5-shot prompting actually reduced performance compared to minimal prompt baseline when using reasoning models.
The R1 release paper from DeepSeek reached a similar conclusion, observing that few-shot prompting consistently degrades performance and recommending users directly describe the problem and specify the output format using a zero-shot setting for optimal results.
Why This Happens
Reasoning models like OpenAI's o1 and DeepSeek's R1 have built-in chain-of-thought capabilities. Adding examples:
Clutters their reasoning process
Provides conflicting patterns
Wastes their reasoning tokens
Reduces overall quality
OpenAI's Guidance
Findings align with OpenAI's guidance: "Limit additional context in retrieval-augmented generation (RAG): When providing additional context or documents, include only the most relevant information to prevent the model from overcomplicating its response".
The New Rule for Reasoning Models
If you want to try out few shot prompting with reasoning models, start with just an example or two, and see how things go.
Use this decision tree:
For standard models (GPT-4, Claude 3.5, etc.): Use 2-5 examples as normal
For reasoning models (o1, o1-mini, R1): Start with zero-shot; add maximum 1-2 examples only if absolutely necessary
For very simple tasks on reasoning models: Skip examples entirely
Industry Adoption and Statistics
Few shot prompting has moved from research to production.
Market Growth
The global prompt engineering market is experiencing rapid expansion as organizations recognize the value of effective AI interaction methods. Few shot prompting represents a significant portion of this growth.
Performance Data
Closed-source models demonstrated higher accuracy, F1 scores, and robustness, achieving improvements of 5-15% in performance metrics when Chain-of-Thought prompting was employed.
GPT-4o, when prompted with CoT, achieved an F1-score of 99.00% for sentiment analysis and 58.22% for question answering, showcasing its superior reasoning capabilities.
Company Adoption
Companies like Bolt and Cluely report that system prompts play a huge role, with Cluely's prompts helping reach $6M ARR in just 2 months.
The best AI companies are obsessed with prompt engineering, with prompt engineering serving as product strategy in disguise where every instruction written into a system prompt is a product decision.
Developer Trends
In 2025, every product manager needs to be good at prompt engineering—it's not something you can just outsource to engineering.
The ability to effectively use few shot prompting has become a core skill for:
AI product managers
Developer advocates
Customer success engineers
Technical writers
Data analysts
Future Outlook
Where is few shot prompting headed?
Near-Term Trends (2025-2026)
1. Automated Example Selection Auto-CoT automatically generates reasoning chains for demonstrations by leveraging LLMs, sampling questions with diversity and generating reasoning chains to construct demonstrations. Similar automation will emerge for few-shot example selection.
2. Dynamic Example Retrieval Systems will increasingly use vector stores with semantic search to automatically find the most relevant examples from large databases rather than manually selecting them.
3. Hybrid Approaches Combining few-shot with RAG (Retrieval-Augmented Generation) will become standard, pulling relevant examples from knowledge bases in real-time.
4. Cost Optimization Tools will emerge to analyze token usage and recommend optimal example counts, balancing quality against cost.
Longer-Term Evolution (2027+)
Model Memory: Future models may "remember" examples across sessions, reducing the need to repeat them in every prompt.
Meta-Learning Integration: Models may develop better few-shot capabilities through meta-learning during pre-training, requiring fewer examples for the same quality.
Specialized Few-Shot Models: We may see models specifically optimized for few-shot learning in particular domains (medical, legal, code).
The Caveat
As reasoning models evolve, the role of few-shot prompting may diminish for complex cognitive tasks while remaining essential for formatting and stylistic consistency.
FAQ
1. What's the difference between few-shot prompting and few-shot learning?
Few-shot prompting is a technique you use when interacting with an already-trained model—you provide examples in your prompt. Few-shot learning is a machine learning paradigm where you train a model to learn from very few examples. Prompting requires no training; learning does.
2. Can I use few-shot prompting with any AI model?
Most modern large language models support few-shot prompting (GPT-3.5, GPT-4, Claude, Llama, etc.). However, newer reasoning models like o1-preview and o1-mini may see reduced performance with few-shot prompting compared to zero-shot approaches.
3. How do I know if I need few-shot instead of zero-shot?
Try zero-shot first. If results are inconsistent, don't match your format, or lack the right tone, switch to few-shot. Research by Reynolds and McDonell in 2021 found that with improvements in prompt structure, zero-shot prompting can outperform few-shot prompting in some scenarios.
4. Does the order of examples matter?
Yes, order matters, with research demonstrating that a model's predictions varied dramatically based on the sequence of examples—the right permutation led to near state-of-the-art performance while others fell to nearly chance levels. Place your best or most important example last.
5. Can few-shot prompting replace fine-tuning?
For many tasks, yes. For mission-critical applications requiring maximum accuracy, fine-tuning still wins. Few-shot is faster, cheaper, and more flexible; fine-tuning is more accurate and faster at inference.
6. How many tokens do examples typically use?
This varies wildly based on example length. A simple sentiment example ("Great! → Positive") might use 5-10 tokens. A code example with comments could use 100-200 tokens. Always test with a tokenizer tool.
7. Can I mix different task types in one prompt?
Not recommended. Keep one prompt focused on one task. Mixing tasks (e.g., sentiment + translation + summarization) confuses the model and degrades performance.
8. What if my examples have errors?
The model will replicate them due to garbage in-garbage out. Always validate example accuracy before using them in production.
9. Can I use few-shot prompting for creative tasks like storytelling?
Yes! Few-shot works excellently for establishing tone, style, and structure in creative writing. Provide examples in the voice and format you want.
10. Does few-shot prompting work in languages other than English?
Yes, though performance may vary by language. Models like GPT-4 and Claude 3.5 have strong multilingual capabilities and can learn from examples in many languages.
11. How do I handle tasks with multiple correct outputs?
Include examples showing the variety of acceptable outputs. This teaches the model the range rather than one rigid answer.
12. Can I reuse the same examples across different queries?
Absolutely. That's one of the key benefits. Create reusable prompt templates with proven examples for recurring tasks.
13. What if adding examples makes performance worse?
This can happen with: (1) reasoning models, (2) very simple tasks where examples add noise, or (3) poorly chosen examples. Test systematically—sometimes zero-shot is better.
14. How do I measure few-shot prompting effectiveness?
Create a test set of 20-100 examples with known correct outputs. Run your prompt on all of them. Calculate accuracy, precision, recall, or whatever metric fits your task. Compare against zero-shot and alternative approaches.
15. Does few-shot prompting handle ambiguity well?
Better than zero-shot but not perfectly. If your task has inherent ambiguity, show examples that demonstrate how you want ambiguous cases handled.
Key Takeaways
Few shot prompting teaches AI through 2-5 examples embedded directly in your prompt, enabling consistent outputs without expensive model training
Originated from 2020 GPT-3 research demonstrating that large language models could perform tasks through in-context learning alone
Dramatically outperforms zero-shot approaches for structured outputs, tone matching, and complex formatting tasks
Optimal range is 2-5 examples—research shows diminishing returns beyond this, with 8 being the practical maximum
Quality trumps quantity—diverse, relevant examples beat numerous similar ones every time
Example order matters significantly—place your strongest example last to maximize impact
Reasoning models are the exception—OpenAI's o1 and similar advanced models perform better with zero-shot or minimal examples
Cost-benefit analysis matters—every example consumes tokens; balance quality needs against API costs at scale
Not a replacement for fine-tuning—but serves as excellent middle ground between zero-shot and full model training
Future is hybrid approaches—combining few-shot with RAG, chain-of-thought, and dynamic example selection
Actionable Next Steps
Ready to implement few shot prompting? Follow these steps:
Audit Your Current Prompts
Identify which prompts produce inconsistent results
Note where formatting varies
List tasks requiring specific tone or style
Start Small with One Task
Choose your most problematic prompt
Write 2-3 high-quality examples
Test on 10 real inputs
Measure improvement
Build a Prompt Library
Create reusable templates for common tasks
Document which examples work best
Share effective patterns with your team
Version control your prompts
Test Systematically
Create test sets with ground truth answers
Compare zero-shot vs few-shot performance
Track metrics: accuracy, consistency, cost
Iterate based on data, not guesswork
Optimize Token Usage
Use tokenizer tools to measure costs
Shorten examples without losing clarity
Remove redundant examples
Balance quality and expense
Scale Gradually
Validate approach on one use case
Expand to similar tasks
Build internal best practices guide
Train team members on effective techniques
Monitor and Maintain
Track performance metrics over time
Update examples as edge cases emerge
Adjust for model updates
Gather user feedback continuously
Combine Techniques
Experiment with few-shot + chain-of-thought
Try few-shot + RAG for knowledge tasks
Use few-shot for formatting, zero-shot for reasoning with advanced models
Learn from the Community
Follow prompt engineering resources
Share findings with colleagues
Test new techniques as they emerge
Stay updated on model capabilities
Know When to Graduate
If accuracy plateaus below needs, consider fine-tuning
If costs become prohibitive, explore model optimization
If examples exceed 8-10, rethink your approach
Glossary
Chain-of-Thought (CoT): A prompting technique where the model shows its reasoning process step-by-step, often combined with few-shot examples.
Context Window: The maximum amount of text (measured in tokens) a model can process in one prompt, including your examples and query.
Fine-Tuning: The process of retraining a pre-trained model on thousands of task-specific examples to specialize its capabilities.
In-Context Learning (ICL): The ability of language models to learn from examples provided within the prompt without updating model parameters.
One-Shot Prompting: Providing exactly one example to guide the model's behavior.
Prompt Engineering: The practice of crafting effective prompts to get desired outputs from AI models.
RAG (Retrieval-Augmented Generation): A technique that retrieves relevant information from a database to enhance the model's responses.
Token: The basic unit of text that language models process; roughly 0.75 words in English.
Vector Store: A specialized database that stores information as numerical vectors, enabling semantic search and similarity matching.
Zero-Shot Learning: Asking the model to perform a task without providing any examples, relying solely on its pre-trained knowledge.
Zero-Shot Prompting: The practice of giving a model a task with clear instructions but no examples to learn from.
Few-Shot Learning (FSL): A machine learning paradigm where models are trained to learn from very few examples of each class.
Demonstration: Another term for the examples you provide in few-shot prompts; shows the model what behavior you want.
Autoregressive Model: A type of language model that predicts the next token based on previous tokens, used in models like GPT.
Gradient Updates: Changes to a model's internal parameters during training; few-shot prompting works without these.
Sources & References
Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., et al. (2020). "Language Models are Few-Shot Learners." Neural Information Processing Systems (NeurIPS 2020). Available at: https://arxiv.org/abs/2005.14165 (Published: May 28, 2020)
IBM. (2025). "What is few shot prompting?" IBM Think Topics. Available at: https://www.ibm.com/think/topics/few-shot-prompting (Published: July 14, 2025)
Learn Prompting. "Shot-Based Prompting: Zero-Shot, One-Shot, and Few-Shot Prompting." Learn Prompting Documentation. Available at: https://learnprompting.org/docs/basics/few_shot
PromptHub. (2024). "The Few Shot Prompting Guide." PromptHub Blog. Available at: https://www.prompthub.us/blog/the-few-shot-prompting-guide (Published: April 2024)
Prompt Engineering Guide. "Few-Shot Prompting." Prompt Engineering Guide. Available at: https://www.promptingguide.ai/techniques/fewshot
Shelf. (2025). "Zero-Shot vs. Few-Shot Prompting: Key Differences." Shelf Blog. Available at: https://shelf.io/blog/zero-shot-and-few-shot-prompting/ (Published: February 11, 2025)
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Wei, J., Wang, X., Schuurmans, D., et al. (2022). "Chain-of-thought prompting elicits reasoning in large language models." Advances in Neural Information Processing Systems, 35, 24824-24837. Available at: https://www.promptingguide.ai/techniques/cot
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Vellum AI. "Chain of Thought Prompting (CoT): Everything you need to know." Vellum AI Blog. Available at: https://www.vellum.ai/blog/chain-of-thought-prompting-cot-everything-you-need-to-know
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Touvron, H., et al. (2023). Referenced in Prompt Engineering Guide regarding when few-shot properties emerged in scaled models.
Kaplan, J., et al. (2020). "Scaling Laws for Neural Language Models." Referenced in research about model scaling and few-shot capabilities.
Labelbox. "Zero-Shot Learning vs. Few-Shot Learning vs. Fine-Tuning: A technical walkthrough using OpenAI's APIs & models." Labelbox Guides. Available at: https://labelbox.com/guides/zero-shot-learning-few-shot-learning-fine-tuning/
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