Prompt Engineering Skill

Chain-of-thought prompting guides a language model to break down complex reasoning into intermediate steps. Use it for multi-step logical tasks where zero-shot prompting yields vague outputs or incorrect interpretations, ensuring accurate, structured results.

Structure an effective prompt for AI code review by defining the exact task criteria, such as security and performance, providing context, and requesting a specific output format like JSON. This ensures actionable, structured feedback from the language model.

Defend against prompt injection in multi-turn conversations by implementing system prompt patterns and boundary constraints. This prevents malicious inputs from overriding your original instructions and maintains the integrity of the language model interaction.

The essential components of prompt anatomy are System instructions, Context, Examples, Task definition, and Format specification. Structuring prompts with these elements ensures the language model interprets your intent accurately and generates optimal results.

Tune temperature and parameters for iterative prompt refinement by adjusting model settings to control output randomness. Lower temperatures yield deterministic responses for factual tasks, while higher temperatures foster creative generation, allowing you to evaluate metrics and iterate.

Few-shot prompting provides multiple examples within the prompt to guide the language model output, while zero-shot relies on direct instructions without examples. Choose few-shot for complex pattern recognition and zero-shot for straightforward instruction tuning.