Research-Backed Prompt

Few-Shot In-Context Learning (Pattern Recognition Anchoring)

Provide 2-3 high-quality examples before the task. Improves output quality by 22-47% without retraining. The model learns pattern from examples. Critical for consistent formatting and style.

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Few-Shot In-Context Learning (Pattern Recognition Anchoring)

Provide 2-3 high-quality examples before the task. Improves output quality by 22-47% without retraining. The model learns pattern from examples. Critical for consistent formatting and style.

few-shot

pattern-learning

in-context

consistency

formatting

style-transfer

Variables

First Example - Input *

First Example - Output *

Second Example - Input *

Second Example - Output *

Third Example - Input *

Third Example - Output *

Actual Task to Complete *

Prompt

Learn from these examples, then apply the same pattern:

---EXAMPLES---

Example 1:
Input: 
Output:


Example 2:
Input: 
Output:


Example 3:
Input: 
Output:


---NOW APPLY THIS PATTERN---

Input: 
Output:

Examples

Input

{
  "example1Input": "User complaint: 'Your API is too slow'",
  "example1Output": "Problem: Performance\nSeverity: High\nCategory: Backend\nAction: Profile endpoints, add caching",
  "example2Input": "Feature request: 'Can you add dark mode?'",
  "example2Output": "Problem: UX Enhancement\nSeverity: Low\nCategory: Frontend\nAction: Design tokens, test across devices",
  "example3Input": "Bug report: 'Login fails on Safari'",
  "example3Output": "Problem: Browser Compatibility\nSeverity: Critical\nCategory: Auth\nAction: Test Safari, check cookie handling",
  "actualTask": "User says: 'Database queries timeout after 30 seconds'"
}

Output

Problem: Database Performance
Severity: Critical
Category: Backend
Action: Optimize queries, add indexes, implement connection pooling

Sources

https://arxiv.org/abs/2005.14165

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