Here are ten important aspects for prompt engineering and approaches to tweak prompts for concise and pertinent answers from Large Language Models (LLMs):

1. Clarity: Ensure your prompt is clear, specific, and unambiguous. Use simple and direct language to avoid confusion.

2. Context: Provide relevant context to help the LLM understand the purpose and scope of the task. Include necessary background information.

3. Desired output format: Specify the desired format of the response, such as a list, paragraph, or step-by-step instructions.

4. Word count or response length: Set a word count limit or specify the desired response length to encourage concise answers.

5. Relevant examples: Provide examples of the type of response you expect to guide the LLM towards generating similar outputs.

6. Keyword emphasis: Highlight or emphasize important keywords or phrases to draw the LLM's attention to key aspects of the prompt.

7. Explicit instructions: Include explicit instructions or directives to guide the LLM's behavior, such as "summarize," "compare," or "analyze."

8. Avoid open-ended questions: Minimize open-ended questions and instead use specific, targeted prompts that elicit focused responses.

9. Iterative refinement: Begin with a general prompt and iteratively refine it based on the LLM's responses to improve accuracy and relevance.

10. Domain-specific language: Use domain-specific terminology and language that is relevant to the task at hand to improve the pertinence of the responses.

To tweak prompts for concise and pertinent answers, consider the following approaches:

• Add constraints or limitations to the prompt, such as word count or time period.
• Break down complex tasks into smaller, more focused sub-tasks.
• Use follow-up prompts to refine or clarify the initial response.
• Experiment with different wordings or phrasings to find the most effective prompt.
• Provide feedback to the LLM on the quality and relevance of its responses to help it learn and improve over time.