prompt-engineer

To reduce token cost in AI prompt development, apply context compression, enforce output constraints, and isolate static sections. This minimizes token usage while maintaining prompt performance during iterative testing and validation workflows.

Selecting a prompt pattern requires matching task complexity to the appropriate structure, choosing between zero-shot, few-shot, Chain of Thought (CoT), Tree of Thoughts (ToT), or ReAct patterns to optimize AI model performance.

Running A/B testing for AI prompt variants involves building a comprehensive test set and systematically evaluating different prompt structures to measure and improve model performance before deployment.

Automated scripts are used to streamline the prompt optimization process, handling pattern selection, token reduction, and systematic variant testing to ensure efficient prompt development without manual overhead.

Chain of Thought (CoT) or ReAct patterns are selected over few-shot prompting when task complexity increases, requiring more advanced reasoning structures to achieve optimal AI model performance and accurate outcomes.