Prompt Engineering Patterns #
Prompt engineering is the practice of designing, refining, and optimizing inputs to language models to achieve desired outputs. This section covers patterns and techniques for effective prompt design.
Why Prompt Engineering Matters #
- Reliability: Well-designed prompts produce more consistent results
- Performance: Good prompts can dramatically improve model outputs without changing the model
- Cost Efficiency: Effective prompts can reduce token usage and model complexity requirements
- Control: Prompts give you control over AI behavior without needing to modify model weights
Core Prompt Engineering Concepts #
1. Prompt Components #
Most effective prompts include some combination of:
- Instructions: Clear directions on what to do
- Context: Background information needed
- Examples: Demonstrations of desired inputs/outputs
- Input: The specific query or content to process
- Output Format: Specification of how the response should be structured
2. Prompt Techniques #
- Zero-shot: Asking the model to perform a task without examples
- Few-shot: Providing examples within the prompt
- Chain-of-thought: Guiding the model through step-by-step reasoning
- ReAct: Combining reasoning and action steps