The more specific and informative a prompt is, the better the LLM will be able to understand and respond to it. For example, instead of prompting the LLM to "write a poem," you could prompt it to "write a poem about a cat who is lost in a big city."

The more context the LLM has, the better it will be able to understand and respond to the prompt. For example, if you are prompting the LLM to answer a question, you could provide it with the text of the question as well as the relevant background information.

Priming is the process of feeding the LLM a set of examples before the prompt. This can help the LLM to understand the desired output. Fine-tuning is the process of training the LLM on a specific dataset. This can help the LLM to improve its performance on a specific task.

1. What is the desired output? What do we want the LLM to generate?

2. What information does the LLM need to generate the desired output? What kind of context   or background information does the LLM need to know?

3. How can we format the prompt in a way that is clear and concise? We want to make sure    that the LLM understands what we are asking it to do.

• Write a poem about a cat who is lost in a big city
• Write a summary of the article "The Future of Artificial Intelligence."
• Write a code snippet to calculate the factorial of a number.
• Write a script for a short film about two friends who go on a road trip.

• Write something creative.
• Answer my question.
• Tell me a story.
• Generate some text.

• Be specific and clear in your prompt. Tell the LLM exactly what you want it to generate.
• Provide the LLM with the necessary context and background information.
• Use examples to illustrate what you are looking for. This can help the LLM to generate the desired output.
• Break down complex tasks into smaller, more manageable tasks. This will make it easier for the LLM to generate the desired output.
• Use feedback to refine your prompts. If the LLM does not generate the desired output, try to identify the reason why and refine your prompt accordingly.

1. LLMs are complex and opaque. It is difficult to understand exactly how LLMs work and how they process prompts
2. LMs can be biased. LLMs are trained on massive datasets of text and code. This data can reflect the biases of the people who created it. As a result, LLMs can generate biased or inaccurate text. It is important to be aware of these biases and to take steps to mitigate them.
3. Prompt engineering can be time-consuming and computationally expensive
4. Prompt engineering is a challenging task because it requires a deep understanding of both the task at hand and the capabilities of the LLM.