Prompt Engineering Basics: The 'CRISPY' Framework
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Beyond CRISPY, check out LangChain or Semantic Kernel if you're looking to automate prompt chaining or integrate LLMs into more complex applications. They abstract a lot of this framework's concepts programmatically.
I disagree with the idea that 'Role' is always necessary. For many simple data extraction or summarization tasks, assigning a persona just adds unnecessary overhead and can sometimes even bias the output. Simpler prompts are often better for straightforward tasks.
Awesome framework! I usually just dump everything into one giant prompt. This structured approach makes so much more sense. My prompts always end up being super long and convoluted. CRISPY looks like it'll really help streamline my thinking process.
Wait, 'Steps' sounds a lot like 'Instructions' but broken down. Could you explain the distinction a bit more? Like, why not just make instructions more detailed?
I found that adding a 'Tone' parameter (like 'P' for Parameters) is also super helpful for certain outputs. E.g., 'Write this in a playful, informal tone' can drastically change the AI's output beyond just the content.
Good question! Think of it this way: 'Instructions' are *what* to do (e.g., 'Analyze the report'), while 'Steps' are *how* to do it if the task is complex (e.g., '1. Summarize key findings. 2. Identify risks. 3. Propose mitigating actions.'). Hope that clarifies!
This is great! I've been struggling with vague outputs from ChatGPT, and I think 'C' (Context) and 'Y' (Yield) are where I fall short most often. Specifically, defining the desired output format is something I always forget until the AI gives me something I can't use.
Abdelrhman Rabea




