Few-Shot Learning

Guiding LLM behavior through carefully curated examples.

Quick Start

Classify the sentiment of each review:

Review: "This product is amazing!"
Sentiment: Positive

Review: "Terrible experience, would not recommend."
Sentiment: Negative

Review: "It's okay, nothing special."
Sentiment: Neutral

Review: "Best purchase I've made all year!"
Sentiment:

When to Use

• Teaching new tasks without fine-tuning
• Specifying output format by example
• Handling edge cases
• Setting response tone and style

Best Practices

Example Selection

• Cover edge cases and boundary conditions
• Include 3-5 diverse examples
• Show both positive and negative cases
• Order from simple to complex

Formatting

• Use consistent separator between examples
• Clearly mark input vs output boundaries
• Keep format identical to expected real use

Dynamic Selection

• For large datasets: retrieve most similar examples
• Use embedding similarity for example selection
• Limit to context window constraints

Dependencies

pip install openai
# For dynamic selection: pip install sentence-transformers

Examples

Translate English to French:

English: "Hello"
French: "Bonjour"

English: "Good morning"
French: "Bonjour"

English: "Thank you"
French: "Merci"

English: "How are you?"
French:

Resources

• [OpenAI Few-Shot Guide](https://platform.openai.com/docs/guides/prompt-engineering)

Step-by-Step

1. Decompose the task: identify input/output shape and the boundary cases that must be taught.

2. Curate 3-5 examples: pick diverse pairs, ordering simple to complex, including edge cases and counterexamples.

3. Format consistently: use an identical separator between examples and mark input vs output boundaries explicitly.

4. Size to the context: keep examples compact; for larger corpora, retrieve the top-k similar examples via embeddings.

5. Prompt the model with the examples inline (few-shot) or in a system message; keep the target format in the final turn.

6. Evaluate: run on a held-out set, compare formats, then iterate — adding failing cases to the example pool.

# Dynamic few-shot selection using embedding similarity
from sentence_transformers import SentenceTransformer
import numpy as np

model = SentenceTransformer("all-MiniLM-L6-v2")

def select_examples(query: str, pool: list[tuple[str, str]], k: int = 3) -> list[tuple[str, str]]:
    # pool: list of (prompt_example, answer_example) with prompts embedded offline
    emb_q = model.encode(query)
    emb_pool = model.encode([p for p, _ in pool])
    idx = np.argsort([np.dot(emb_q, e) for e in emb_pool])[-k:][::-1]
    return [pool[i] for i in idx]

# Consistent formatting with clear markers
## Example 1
Input: "The movie was brilliant and moving."
Output: Positive

## Example 2
Input: "Waste of money, don't buy."
Output: Negative

## Example 3 (edge case)
Input: "Not bad at all."
Output: Positive

Validation

1. Model follows example patterns consistently

2. Edge cases handled correctly

3. Adding more examples improves accuracy

4. Format matches examples exactly