In prompt engineering, it's crucial to provide examples of desired outputs, as this helps the AI reason through complex tasks and improves the overall quality of its responses.

It's important to evaluate prompts by testing them in real scenarios, as this can reveal flaws in the prompt design that need addressing.

You can enhance your prompting by including examples in your prompt. For instance, provide a problem and a good answer, and then ask the model to solve a similar problem.

Reading prompts and model outputs closely helps improve prompting skills; experimentation is key.

There's a risk of overcomplicating prompts by trying to anticipate every possible issue the model might face, which can lead to ineffective results.

The five effective prompting techniques are: 1) Few-shot prompting, where you give examples of what good looks like; 2) Decomposition, which involves solving sub-problems first; 3) Self-criticism, where you check and reflect on your response; 4) Providing additional context about the problem; and 5) Ensembling, where you try different roles and models to find common answers.

When prompting, I try to give examples that are not too similar to the data the model will see, to avoid overly consistent responses.

In conversational prompt engineering, I often just type a short prompt like 'email' without much detail, and it still works effectively.

Developer tools for prompt engineering are gaining traction, allowing users to chain prompts and test their effectiveness, which is appealing to college students experimenting with AI.