Zero-Shot Prompting
Zero-shot prompting is asking AI to perform a task without providing any examples of the desired output. The model relies entirely on its training to understand and complete the request. It's the simplest prompting approach and works well for common, well-understood tasks.
Example
Asking 'Write a function that calculates the factorial of a number' without showing any example factorial implementations — the AI knows what factorial means from training.
Zero-shot prompting is the most common way people interact with AI. No examples, no special formatting — just describe what you want.
When Zero-Shot Works
- Common tasks — Standard coding patterns
- Clear requests — Well-defined, unambiguous goals
- Established concepts — Things AI saw often in training
- Simple transformations — Straightforward conversions
When to Use Few-Shot Instead
Zero-shot struggles with:
- Custom formats — Your team's specific conventions
- Unusual patterns — Non-standard approaches
- Precise output — When exact formatting matters
- Novel tasks — Things AI hasn't seen before
Improving Zero-Shot Prompts
Even without examples, you can improve results:
Be specific:
- ❌ "Write a function"
- ✅ "Write a TypeScript function that validates email addresses, returns a boolean, and handles edge cases like empty strings"
Provide context:
- ❌ "Fix this bug"
- ✅ "This function should return an array but it's returning undefined. Here's the code..."
State constraints:
- ❌ "Make an API call"
- ✅ "Make an API call using fetch, handle errors, and return typed data"
Zero-Shot vs Few-Shot Tradeoffs
| Zero-Shot | Few-Shot |
|---|---|
| Faster to write | More setup time |
| Less context used | Uses more tokens |
| May miss specifics | More precise output |
| Good for common tasks | Good for custom patterns |
Start with zero-shot. Add examples only when results aren't meeting your needs.
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Related Terms:
Emergent — An agentic vibe-coding platform that builds production-ready apps