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AI Literacy

Crafting Effective Prompts

[Context] + [specific information] + [intent] + [response format] = perfect prompt- Full video with captions on YouTube (6:26)

 

1 - Be Specific: If you ask a vague question, you are likely to get a vague answer. The more details you provide, the better the model can give you what you're looking for. Instead of "tell me about all dog breeds that exist,” ask "What are the different breeds of small dogs suitable for apartment living?"

2 - State Your Intent: If there's a specific purpose for your question, state it in the prompt. For example, instead of asking “explain quantum physics” you could say "I'm helping my fifth-grade son with his science homework. Could you explain quantum physics in a simple way?"

3 - Use Correct Spelling and Grammar: While the model can often interpret and correct spelling and grammar mistakes, providing clear and correct prompts helps ensure you get the best response.

4 - Direct the Output Format: If you want the answer in a specific format, state it in your question. For example, you could ask "Could you list the steps to bake a chocolate cake?" or "Could you explain the process of baking a chocolate cake in a paragraph?"

5 - Ask Follow-Up Questions: If the response wasn't what you expected or if you need more information, ask follow-up questions to clarify.

6 - Experiment with Different Phrasings: If you're not getting the response you want, try asking the question in a different way. The model's results vary based on your input.

7 - Fact-Check: You can ask the model to fact-check its answer but this is not always effective, so verify independently. Prompt the model to tell you "I don't have enough information" if it is not confident it can formulate accurate answers, this techniques is often effective. Read our Fact Checking AI page for specific techniques on verifying answers.

Adapted from App of the Day

Problem Formulation— creating effective prompts across platforms and iterations

Effective prompt crafting is a moving target due to ongoing developments and shifts in AI. A more "enduring and adaptive skill" is problem formulation (Acar, 2023). Solving problems seems to get all the attention but our ability to formulate problems is fundamental to solving problems and is key to designing a effective prompt and developing this underlying skill will make it more adaptable.

According to Oguz Acar, problem formulation has 4 key elements—

Problem diagnosis is about identifying the core problem for AI to solve. In other words, it concerns identifying the main objective you want generative AI to accomplish. This can be very simple but if your AI output is off-focus, start here as you analyze how you can change your input to get the output you want. More complex problem diagnosis might include process that digs into the "why" behind the problem.

Problem decomposition refers to breaking down complex or multifaceted problems into smaller, single-focus sub-problems. If you enter a complex prompt your AI response is likely to be too broad and generic to be useful. Think carefully through all the component parts that you can address them individually in your input. 

Problem reframing involves changing the perspective from which a problem is viewed. This allows alternative interpretations. Reframe problems by taking on a different perspective or exploring analogies or parallel examples. If your AI output is missing important information, consider reframing your problem so your input provides this extra context.

Problem constraint focuses on determining the boundaries of a problem by defining input, process, and output restrictions of the solution. Some constraints are determined by the task at hand, but your creative goals can also be used to determine what constraints are needed. 

 

Acar, O. A. (2023, June 6). Ai prompt engineering isn’t the future. Harvard Business Review. https://hbr.org/2023/06/ai-prompt-engineering-isnt-the-future