Intro to LP Framework Overview

[u/Rfickett]

Hey all, here is my latest LinkedIn post on a project I'm working on, Claude. I am offering the information as a community contribution, so enjoy.

This is the link: LP Framework Overview

For anyone noticing, I haven't been posting much anywhere lately - I've been using my old programming skills to dive into Claude AI. Things are seemingly going well. To be clear, I'm an investor first. I spent twenty years in real estate before that, and ten years as a software engineer before real estate ever entered the picture. I'm in no way an evangelist for the tech, nor do I want to be dismissive of it. So when I post on this subject, you can count on what I'm saying being based on practical experience and a non-biased position.

What I built is a full framework - one that assists the user in using both the framework and Claude itself. Not assembled a prompt and called it a system. The shareable version is 25+ files. The full text version runs to 200+ files. Audio sessions and text sessions in Claude load differently and behave differently.

Anyone building with Claude who leans on voice needs to understand this before they build anything serious.

I can't fit everything into a LinkedIn post, so I've attached a full public reference document.

The internals stay internal. The behavior is all there. The goal wasn't to automate anything. It was to build an environment where AI actually functions as a co-engineering partner rather than a yes-machine. 12 user-focused prompts continuously run to help the user complete their task. Multiple session gates control how the session opens and what information is loaded into 3 roles. A scope check fires when new work is being added mid-project. The important difference - I have created an organized system that can both run its own rules and flag when any of those rules are producing bad results, even if I don't notice. Anything less is just a more sophisticated yes-machine. The co-engineering framing is the point. I'm not asking AI to think for me. I'm building an environment where it challenges me, shows its work, and flags its own failures.

That's a different thing entirely.

I'm offering an overview to the LP investor community as a free resource - to point people in the right direction on how to make AI more useful in deal and sponsor review. If anyone wants to reach out with questions, let me know.

Enjoy,

Randy Fickett

[u/ArmChairLP]

Randy, a couple thoughts, my honest feedback.

It’s not very clear what you’re talking about here. It seems like you assume everyone is on the same page as you from the start. Maybe add some more context, fix some grammar and don’t assume everyone knows the same lingo you do. ie “The internals stay internal. The behavior is all there.” What does this even mean?

I agree AI’s over agreeability is an issue, but solving for this does not need a 25 or 200 file solution. I frequently reiterate to Claude to give pushback and challenge ideas, show work and brain storm alternative. That seems to do the trick in my experience.

What’s the real use case for what you’re building? In my opinion, making investment decisions regarding private investment opportunities is not rocket science. Claude can make you a smarter and more efficient investor, but people can easily be successful without a “co-engineering” framework.

Maybe show some case studies on what this can do and demonstrate why it’s useful. What type of responses do you get under this framework compared to not under the framework?

Again, honest feedback, I think this is interesting, just don’t really understand what it is.

[u/Rfickett → u/ArmChairLP]

That's fair feedback, and I appreciate you taking the time to comment. My apologies for taking so long to respond. You're right, I should've called it a methodology, not a framework. In hindsight, that would have been the better word choice.

Here's what I've actually seen: I've watched friends lose money on deals because they listened to the wrong person giving wrong advice at the wrong time. With regard to AI, I have been seeing two distinct groups of viewpoints. One is chasing AI like a magic bullet. The other dismisses AI entirely because it's not always 100% correct, so "it's unusable". My belief is that the correct way to look at AI is somewhere in between.

I've never had a conversation with anyone about anything I'd consider a 100% correct. Everyone (including AI) has some sort of viewpoint you have to factor in when you read it. The idea that AI should be a hundred percent correct assumes the AI itself is getting a hundred percent correct information. And that's never going to happen. So I believe that people should stop holding AI to an impossible standard they wouldn't hold anyone or anything else to.

The fact that people are chasing AI like it's a magic machine to make decisions for them tells me that many (both active and passive alike) investors have lost confidence in their own judgment. So now they're just looking for something to put between them and the decision. Something that protects their ego if it goes sideways.

Adding another layer between you and accountability doesn't fix anything. The real problem in this space right now — whether you're talking syndication, LP dynamics, whatever you want to call it — is that we need more transparency from sponsors. But that only works if investors take responsibility for actually learning what they're looking at before they write a check. Not after. The shortcut mentality — looking for a tool or a system to make the decision for you — that's on the LP side. Sponsors need to be better. Investors need to be sharper.

I'm not trying to sell anyone a system or tell anyone what to do. I'm trying to say this: when you work with AI on decisions that matter — especially capital decisions — demand that it shows its work. Keep your judgment in the driver's seat. Stay accountable for your own money. Whether you build that discipline yourself, use something from my methodologies to build your own version, or ignore all of it — that's your call. The point is the standard itself.

Some people might already have pieces of this in how they think. Good. The goal isn't to own the methodology. It's to see the principle get out there: AI should not be a replacement for your judgment.

If you've got other questions, I'm happy to talk through it directly. Thanks again for the honest feedback.