Advanced Prompt Engineering
Zero-shot and Few-shot Learning
Mastering the art of context-based prompting.
Interview: High - Most common prompting technique
Prompting Without Training
Zero-shot vs Few-shot
One of the most powerful capabilities of LLMs is their ability to perform tasks they were never explicitly trained on, using only instructions or examples in the prompt.
Zero-shot Prompting
Give the model a task description with no examples. Works well for common tasks.
Few-shot Prompting
Provide 2-5 examples of input-output pairs before your actual query. The model learns the pattern from examples and applies it.
Best Practices
- Use consistent formatting across all examples
- Include edge cases in your examples
- Order examples from simple to complex
- Match the diversity of examples to your expected input distribution
Use Cases
Text classification without training data
Data extraction from unstructured text
Format conversion (JSON, CSV, etc.)
Translation between domain-specific formats
Common Mistakes
Using too many examples (>5) which wastes tokens without improving quality
Examples that contradict each other confuse the model
Not testing with zero-shot first — sometimes it works just as well