By skyforbes I’ve been experimenting with a structured reasoning prompt based on LERA Framework to help ChatGPT handle complex or messy problems more clearly.
It forces the model to break things down into:
- goals
- risks
- dependencies
- system boundaries
- long-term effects
I’m curious how well this works across different domains (EV builds, engineering, life decisions, productivity, startups, relationships… anything really).
Here’s the prompt:
“Use the LERA framework to analyze my problem.
Break it down into:
– goals
– risks
– dependencies
– system boundaries
– long-term effects
Here is my situation: [describe your problem]”
Looking for testers in EV, batteries, motors, thermal issues, reliability, etc.
If you’re willing, try it on ANY real problem you have.
Post the prompt + ChatGPT’s output in the comments.
I want to see:
– where it works well
– where it breaks
– any surprising insights
– domains where the structure is especially useful
If this gets enough examples, I’ll compile the best ones and share the patterns.