Whether you’re new to prompting or a prompting veteran, frameworks can be super helpful to get more reliable and consistent results from your AI models.
The core idea behind RISEN is ensuring you provide the AI with a comprehensive, structured "recipe" for completing a task.
No more vague instructions or half-baked expectations.
RISEN gives you a framework to capture all the essential elements:
If you’re thinking – "That sounds like a lot of work upfront!" I get it and you’re not wrong.
But trust me, the time invested in crafting RISEN-structured prompts pays dividends in the form of higher-quality, more reliable AI outputs.
Role: Chief AI Scientist
Input: A blog post about LLMOps
Steps: Read this blog post. Provide me with a 300-word summary on what the blog entails.
Expectation: a concise, informative summary detailing what LLMOps is, how to implement LLMOps, and how to create alignment with LLMs and my business
Narrowing: Focus on making this summary relevant for a general business audience, avoiding overly technical jargon.
Why you should try a framework
Frameworks like RISEN transform the AI from a black box into a collaborative partner, where you're both working towards a clearly defined goal. The more thoughtful and intentional you are with your prompts, the more valuable the AI's contributions will be.
When I’m experiencing hallucinations with an AI, I lean on frameworks to help the AI narrow in on my request.
Of course, RISEN isn't a silver bullet. There will always be some trial and error, and you have to be diligent about monitoring for potential mistakes or biases.
But as a framework for prompt engineering, it's an absolute game-changer.
So the next time you're working with an AI assistant, whether it's Claude or any other model, give RISEN a try. I guarantee it will elevate the quality, consistency and reliability of your outputs.
And don't be afraid to experiment - the more you use it, the more intuitive it becomes.