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.

It's beneficial for AI applications to infer context and suggest prompts dynamically, rather than relying solely on static examples.

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

Meta prompting involves giving a role to the AI, such as an expert prompt engineer, to enhance the quality of prompts.

The ability to prompt AI effectively is crucial, as it determines the quality of the output, which is a reflection of agency and taste in AI development.

The prompt box in AI interfaces is crucial, and providing pre-built prompts can enhance user experience by guiding them in their input.

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.

Effective prompts often start by defining the role of the AI, detailing its responsibilities, and providing a structured plan for tasks, which helps in guiding the AI's decision-making process.

The general guidance for prompting AI is to ensure that all necessary context is provided, especially when the AI is unfamiliar with the situation.