I should start by saying this: I don’t run R2O like a rigid seven-step checklist. It’s not a project management framework, and it’s definitely not some color-coded spreadsheet with perfect formulas. It’s more of a rhythm I’ve built for myself — a loop that keeps me learning fast, interrogating information, and producing something useful without getting lost in the weeds of a LLM.

If you’ve ever gone down the LLM rabbit hole, you know how easy it is to just consume without actually thinking. You prompt, you skim, you nod along — but you don’t really absorb it. R2O is my way of making sure the learning sticks, that I keep control of the thought process, and that I end up with something tangible at the end.

Here’s how it actually works.

Step 1: Start With a Few Questions

I usually begin with curiosity, not structure. I don’t have a top-five checklist or a perfect research brief. I just have a couple of things I want to understand better. That’s enough.

I’ll open up ChatGPT or Gemini and have a back-and-forth until I land on a strong set of deep research prompts. These aren’t surface-level “what is X” prompts. They’re structured to force the model to pull from multiple angles — history, market context, technical depth, risks, opportunities.

Once I have those, I’ll run them across a few different models — ChatGPT, Gemini, Claude sometimes Grok. The goal isn’t to find “the right answer.” The goal is to see multiple perspectives and to pressure-test them against each other.

What I get back are essentially research reports: 20–40 pages long, full of compiled sources, stats, context, and (sometimes) contradictions. They’re not perfect. They miss things. But they’re a strong starting point.

Step 2: Turn Reports Into a Podcast

This is the secret sauce. Instead of staring at PDFs or scrolling through 40 pages of model output, I load the reports into NotebookLM and spin them into a podcast (You can also automate this, but this is easy enough).

Why a podcast? Because it changes the way I consume. Listening is different from reading — it frees up mental bandwidth for reflection. I throw on headphones, step away from my computer, and just listen.

Here’s the trick: I’m not multitasking. I’m not doing dishes or answering emails. I sit in a chair with my iPad, listening with intent. Whenever something jumps out — a stat, a contradiction, a new angle — I jot it down in a simple bulleted list. No overthinking, no formatting. Just capture.

Then I do a second listen. First pass is broad capture. Second pass is focused: What really matters? What’s surprising? What new questions does this raise? That’s when I start to see the shape of the topic.

Step 3: Notes → Questions → Reflection

The notes are messy on purpose. I don’t worry about formatting or making them presentation-ready. I’ll loosely tag them — facts, quotes, questions, contradictions, implications — but the point isn’t categorization. It’s engagement.

By writing things down, I force myself to actually process them. Why does this stat matter? How does it connect to that other insight? What does this mean for the bigger problem I’m trying to solve?

Naturally, notes turn into questions. Some come immediately (“Wait, how reliable is that number?”). Others surface later, when I’m reflecting. That’s fine — the process is iterative.

Step 4: Interrogation With the LLM

Now comes the interrogation loop. I take my notes and feed them back into a model, but this time I provide context. I’ll say:

• Here are my notes
  
  

• Here are the questions I want to answer
  
  

• Here’s the audience or output I’m working toward
  
  Prompt: “Here are my notes and questions for the attached PDF reports. My goal is to [learn about this topic, write a one-pager, etc.], please ask me one question at a time to fill in any gaps from my notes.”
  
  

• Then I run the loop, one question at a time. I force myself to log claims and evidence, and to check why each stat or point matters.

This is how I avoid blindly trusting model output. I’m constantly asking:

• Is this credible?
  
  

• How does it connect to the rest of what I’ve learned?
  
  

• Do I need to validate this elsewhere?
  
  

It turns the LLM into a partner in interrogation, not just a vending machine for answers.

Step 5: Cycle, Synthesize, Produce

From there, it becomes a cycle:

Research → Podcast → Listen → Notes → Questions → Interrogation → Draft.

Sometimes I loop through again: another round of research, another podcast, more notes. Each cycle deepens the understanding.

Eventually, I get to something concrete:

• A one-pager for internal use.
  
  

• A diligence brief for a client.
  
  

• A thesis snapshot to share with a partner.
  
  

• Or just a sharper mental model that I can use in conversation.
  
  

Before I finish, I always red-team myself: What could make this wrong? What’s missing? What risks haven’t I considered? That final round of self-interrogation keeps me honest.

Why This Works

There are three reasons this method works so well for me:

1. I actually learn.
   
   By listening, reflecting, and taking my own notes, I’m not outsourcing my thinking. The LLM speeds me up, but I stay in control.
   
   

2. It’s iterative.
   
   I don’t need the perfect list of questions up front. I just need curiosity and a willingness to loop. Each cycle compounds my knowledge base.
   
   

3. It’s flexible.
   
   Sometimes it’s lightweight — just me learning something new. Other times it’s formal — building a deck for investors or writing a diligence report. The same process scales up or down depending on what I need.
   
   

The Bottom Line

That’s my Research-to-Output Sprint. Not a rigid checklist. Not a perfect workflow. More like a rhythm: prompt, listen, note, question, interrogate, repeat.

And at the end of it, I don’t just have a document or a slide deck. I’ve actually learned something, discovered something, and made the knowledge my own.