In this fast-evolving world of artificial intelligence, the kind of outputs one will receive from an AI model is highly dependent on the type of input prompts. To have the most excellent effectiveness in using AI tools, the CARE framework includes Context, Ask, Rules, and Examples.
Context In addition, examples are used to contextualize the AI with more elaborate background information and a specific scenario where the prompt will be used. This could be associated with the target audience, the purpose of the task, or any other background information relevant to the context. If you were generating marketing copy for a restaurant with the help of AI, for instance, you’d provide some information on the kind of cuisine the restaurant offers, the ambiance of the place, and the target clientele.
Ask The “Ask” part just puts the objective or question quite clearly. This is what you ask for directly and in a specific manner. If not defined well, you will end up with a wrong answer. As such, defining what you are looking for the AI to do is essential. You do not say, “Write about our new product”; you can say instead, “Write a 200-word description highlighting the key features and benefits of our new product.”
Rules This involves setting rules, which are constraints and guidelines the AI must follow. One may want to set rules relating to preference in style, tone, length, or precise keywords required or not to be used. For instance, you may specify that the output be written formally in less than 300 words and include the terms “innovative” and “cost-effective.”
Examples One way in which an example helps the AI to figure out what the user would like for output is because the former has something it can use as a point of reference. These should be close in kind to what you expect from the prompt. For example, if you are writing a blog post, show an extract that, in tone, style, and structure, closely resembles the extract you would like to get.
Additional Insights on Effective AI Prompt Crafting Sequential Prompting: It involves building a conversation with an AI where, with every new question, the prompt expands the question before. When dealing with complex topics, such as the effects of climate change, it may be helpful to start with a broad prompt and then continue refining one’s query into mitigation strategies that are both accessible and actionable (MIT Sloan TeachLearnTech) (Codecademy).
Creative and Exploratory Prompting: The use of creative prompts by asking, for example, to imagine a world where all power is drawn from renewable energy, will yield alternative perspectives on the course material and also encourage inventiveness in AI.
Chain of Thought Prompting: By breaking a complex problem into small parts, thereby making it practicable, the AI would logically proceed towards achieving a goal. It is particularly effective in detailed research, problem-solving, or creative tasks (Nathan Onn) (Amphy Blog).
Role-Based Prompts: Setting the AI into various roles, such as that of a personal assistant or a legal expert, would help understand more accurately the context and perspective from which it is being answered.
These techniques used with the CARE framework enable users to make prompts that enhance the quality and relevance of AI-generated content by a significant factor. This structured approach not only boosts the precision of outputs but also helps gain more effective achievement of goals. More specifics and details can be found in the original article available on the website of the Nielsen Norman Group.