Hello everyone,
My name is Philip Camps, and I am a solo engineer with Dimenwave (dimenwave). Like many of you, I use LLMs daily to aid in software development, but I’ve always been fascinated by a specific challenge: AI, for all its intelligence, struggles with segmenting information based on its inherent “certainty.”
To tackle this, I’ve developed a proposed framework called the Grounded Intelligence Protocol (GIP). It’s built on a philosophy I call ‘Architectural Humility’ the idea that an AI should inherently recognize the difference between a mathematical certainty and a speculative claim.
I built an interactive implementation to show how this works in practice. The protocol segments data into Tiers (from Immutable Truths to Speculative Claims) and applies verification layers to ensure the AI doesn’t just produce “plausible prose,” but grounded reality.
Check out the live demo here: philphilos github io/gip-interactive-app
I hope this community finds this angle interesting. While I used some AI assistance to build out the UI (I’ll be humble and say I don’t know everything in coding!), the core logic is something I’m passionate about contributing back to the AI safety conversation.
I’d love to hear your thoughts on the tier system or how we might better integrate this type of “grounding circuit” into LLM system prompts.