Few-Shot Prompting: Teaching AI by Example

Few-shot prompting is one of the most effective techniques in the prompt engineering toolkit. By providing a small number of input-output examples before your actual request, you teach the AI model the exact pattern, format, and behavior you expect. This technique bridges the gap between zero-shot simplicity and the heavy investment of fine-tuning, offering a practical middle ground that dramatically improves accuracy and consistency.

Understanding Few-Shot Prompting

Few-shot prompting, also known as in-context learning, leverages the model's ability to recognize and replicate patterns from examples provided directly in the prompt. Instead of relying solely on the model's pre-trained understanding of your task, you show it exactly what good output looks like.

The term "few-shot" typically means providing between two and five examples, though the optimal number depends on the complexity of the task and the context window size of the model. One example is sometimes called "one-shot," while zero examples is "zero-shot." Research consistently shows that even a single well-chosen example can significantly improve performance over zero-shot prompting for many tasks.

"Few-shot prompting transforms the AI from a general assistant into a specialist that understands your exact requirements, without any model training or fine-tuning."

How to Structure Few-Shot Prompts

A well-structured few-shot prompt follows a consistent pattern that makes it easy for the model to identify the input-output relationship:

Task: Classify the following customer messages into categories.

Message: "My order hasn't arrived and it's been two weeks"
Category: Shipping Issue

Message: "The product broke after just one day of use"
Category: Product Quality

Message: "Can I return this item for a full refund?"
Category: Returns

Message: "I was charged twice on my credit card"
Category:

Notice the consistent structure: each example follows the same format, uses the same labels, and the final entry is left incomplete for the model to fill in. This parallel structure is critical because the model identifies the pattern from the formatting as much as from the content.

Selecting the Right Examples

The examples you choose have an outsized impact on the quality of the model's output. Here are the principles for selecting effective few-shot examples:

Diversity of Cases

Your examples should cover different categories, edge cases, and variations of the task. If you are building a sentiment classifier and all your examples are positive, the model may be biased toward positive classifications. Include examples from each category to give the model a balanced understanding.

Representative Complexity

Match the complexity of your examples to the complexity of the actual inputs the model will encounter. If your real-world inputs are long, nuanced, or ambiguous, your examples should reflect that. Simple examples may teach the model to oversimplify its responses.

Correct and Consistent Labels

Every example must be accurate. A single incorrect example can mislead the model and degrade performance across all subsequent predictions. Double-check your examples before including them in the prompt.

Advanced Few-Shot Techniques

Dynamic Example Selection

In production systems, you can dynamically select the most relevant examples for each input using similarity search. By embedding your example library and selecting the examples closest to the current input, you create a few-shot prompt that is maximally relevant. This technique, sometimes called dynamic few-shot or retrieval-augmented few-shot, consistently outperforms static example sets.

Chain-of-Thought Few-Shot

Combining few-shot with chain-of-thought prompting is one of the most powerful combinations in prompt engineering. Instead of just showing input-output pairs, you show the reasoning process that leads from input to output:

Question: If a store has 15 apples and sells 3 bags of 4 apples each,
how many are left?
Reasoning: The store starts with 15 apples. They sold 3 bags with
4 apples each, so they sold 3 x 4 = 12 apples. 15 - 12 = 3 apples remain.
Answer: 3

Question: A train travels 120 km in 2 hours. Then it travels another
90 km in 1.5 hours. What is its average speed?
Reasoning:

Negative Examples

Including examples of incorrect outputs alongside correct ones helps the model understand boundaries. You can show common mistakes and their corrections to help the model avoid pitfalls that simple positive examples would not address.

Common Mistakes to Avoid

Even experienced prompt engineers make these mistakes with few-shot prompting:

• Inconsistent formatting: If your examples use different formats, the model gets confused about which format to follow. Maintain strict consistency.
• Too many examples: More is not always better. Too many examples consume context window space and can overwhelm the model. Start with three to five and only add more if accuracy is insufficient.
• Biased example selection: If all your examples represent easy cases, the model will struggle with hard ones. Include at least one challenging or edge-case example.
• Order effects: The order of examples can influence the model's behavior. Research shows that the last example has the strongest influence, so place your most representative example last.

When to Use Few-Shot vs. Other Techniques

Few-shot prompting is your go-to technique when zero-shot is not producing consistent enough results but the task does not warrant fine-tuning a model. It is especially valuable for classification tasks, data extraction, format standardization, and any scenario where showing the desired output pattern is easier than describing it in words.

Consider moving beyond few-shot to fine-tuning when you need to process thousands of similar inputs with maximum efficiency, when your examples are too complex to fit in a prompt, or when you need the model to learn subtle patterns that cannot be captured in a handful of examples.