which of the following is not a strategy used in prompt engineering

Minimalism: Crafting prompts with the least amount of words necessary to elicit the desired response from the AI.

Iterative Refinement: Continuously tweaking and adjusting prompts based on feedback to improve accuracy and relevance of responses.

Contextual Embedding: Providing sufficient background information and context within the prompt to guide the AI's understanding and responses.

Parameter Tuning: Adjusting the AI model's parameters, such as temperature and max tokens, to influence the style and length of the output.

Role Assignment: Explicitly defining the role of the AI within the prompt (e.g., "You are a helpful assistant...") to shape its responses.

Negative Prompting: Instructing the AI on what not to say or avoid certain types of responses.

Keyword Injection: Strategically placing keywords within the prompt to trigger specific types of information or responses from the AI.

Token Compression: Reducing the number of tokens used in a prompt to minimize computational resources and cost while maintaining response quality.