Few-shot prompting — providing examples in your prompt — is one of the most effective ways to control AI output quality and format. The art is in selecting the right examples.

Zero-Shot vs Few-Shot vs Many-Shot

• Zero-shot — No examples. "Classify this review as positive or negative."
• One-shot — One example. Shows the model the expected format.
• Few-shot (2-5) — Multiple examples covering different cases.
• Many-shot (5+) — More examples for complex or ambiguous tasks.

Selecting Effective Examples

Your examples should:

1. Cover the range — Include easy cases AND edge cases
2. Be representative — Match the distribution of real inputs
3. Show the format — Demonstrate exactly the output structure you want
4. Include reasoning — For complex tasks, show WHY each example has its label

Example: Sentiment Classification

"Classify customer feedback as Positive, Negative, or Mixed.

Feedback: 'The product is amazing but shipping took forever' Classification: Mixed (positive about product, negative about delivery)

Feedback: 'Worst purchase I've ever made. Total waste of money.' Classification: Negative (strong negative sentiment, no positive elements)

Feedback: 'Works exactly as described. Fast delivery too!' Classification: Positive (satisfied with both product and service)

Now classify: 'It's okay I guess. Does the job but nothing special.'"

Dynamic Few-Shot Selection

In production systems, dynamically select examples that are most similar to the current input. Use embedding similarity to find the most relevant examples from a library. This dramatically improves accuracy for diverse inputs.

Anti-Patterns to Avoid

• All positive examples — Model may be biased toward positive classifications
• Too-simple examples — Don't show the model how to handle hard cases
• Inconsistent format — Each example should follow the exact same structure
• Contradictory examples — Conflicting labels confuse the model

The Example Library Pattern

For production use:

1. Build a library of 50-100 high-quality examples
2. Categorize them by input type and difficulty
3. For each new input, select 3-5 most relevant examples
4. Include at least one edge case in every prompt