A Structured Method for Using AI Correctly

How I Use Four Separate AI Workspaces and a Back-and-Forth Review Process to Write Accurate Technical Papers

David Allen LaPoint

PrimerField Foundation

December 31, 2025

A note about the word "canonical": In this paper, "canonical" simply means "the official version I'm working from." It does not mean "the final truth" or "beyond question." It just means "the stable reference copy that doesn't change unless I deliberately change it."

A note about the tools: This paper uses ChatGPT and Claude as examples. These are two AI systems made by different companies. The method works because they think differently and catch each other's mistakes. You could use other AI tools, but they need to be truly independent—built by different teams with different training. If two tools share the same underlying technology, they'll make the same mistakes and won't catch each other's errors.

What makes a good substitute tool: A "Generator" tool (like ChatGPT in this method) needs to handle long conversations, follow detailed instructions, and produce organized drafts. An "Auditor" tool (like Claude) needs to review work fresh without knowing what the Generator discussed, come from completely different developers, and start each review with a clean slate.

Introduction

People often say AI is unreliable—that it makes things up or can't be trusted for serious work. In my experience, the problem usually isn't the AI itself. The problem is how people use it.

After using AI every day for extended periods, I've developed a system that solves this. It does two things: First, I keep my work separated into four different AI workspaces, each with a specific job and strict rules about what it can and can't do. This prevents ideas from one task from accidentally influencing another. Second, when I'm ready to publish something, I run it through a back-and-forth review process between two different AI systems. Each one tries to find problems with the other's work.

This method doesn't require me to trust any single AI answer. That's the point—trust isn't necessary.

The method works because ChatGPT and Claude are built by different companies using different approaches. They're not completely independent—they might make some of the same mistakes on certain topics. But they're different enough that errors slipping past one system are often caught by the other. I'm not counting on perfect independence; I'm counting on useful differences.

The First Principle: AI Is a Tool, Not an Expert

At no point do I treat AI as the final word on anything. I don't treat it as automatically correct or as a replacement for my own thinking. Instead, I use it as: a fast research helper, a calculator and analyst, a writing assistant, and a critical reviewer.

Every step of my process assumes the AI might be wrong. The whole system is designed to expose mistakes rather than hide them. Everything else in this paper builds on that idea.

Part I: The Four-Workspace System

Remember: when I say "canonical" here, I just mean "the stable reference copy"—not "the absolute truth."

For research projects that run over days or weeks, I don't work in one long conversation. Instead, I split my work into four separate AI workspaces. Each workspace has one specific purpose and clear rules about what it's allowed to do.

Why bother with this separation? It prevents several problems: ideas drifting away from what I originally meant, seeing only what I want to see, assumptions from one task leaking into another, and errors quietly spreading without being noticed. Each workspace forces a different way of thinking.

1.1 Workspace 1: Number-Crunching Only

What it's for: Running calculations, checking geometry, analyzing data—pure technical work using only the numbers and information I give it.

The rules: No interpreting what results "mean" for any theory. No expecting certain outcomes. No fitting results into a story. Everything is treated as a test, not as evidence.

Why this helps: It keeps the analysis neutral. The AI can't unconsciously shape results toward what it thinks I want to see.

1.2 Workspace 2: The Reference Copy

What it's for: Storing my theory in a stable form that serves as the official version I compare everything against.

The rules: The content here is treated as "the version we're working from." It doesn't get rewritten or reinterpreted unless I specifically tell it to change. This workspace is not for discussion—it's for storage.

Why this helps: It prevents the theory from slowly changing without me noticing. When I test something, I'm always testing it against the same, unchanging statement of what the theory actually says.

1.3 Workspace 3: Testing and Evaluation

What it's for: Comparing new information, observations, or calculations against the reference copy of my theory.

The rules: This workspace is allowed—even encouraged—to criticize the theory. It must flag any mismatches, missing assumptions, or logical gaps. It must keep evidence separate from interpretation.

Why this helps: It creates a space specifically for trying to prove the theory wrong. Finding problems is just as valuable as finding support.

1.4 Workspace 4: Writing for Publication

What it's for: Turning research, analysis, and conclusions into papers ready for others to read.

The rules: Focus on clarity and precise wording. Claims must be carefully limited in scope. Editing for style must never change the technical meaning. When a paper reaches this stage, it triggers the formal review process described in Part II.

Why this helps: It keeps writing separate from analysis and testing. Papers get produced through a controlled process that doesn't contaminate the technical work.

1.5 Why This System Works

When everything happens in one workspace: assumptions affect calculations, the theory shapes how I interpret results, errors spread without being caught, and the AI's memory of earlier conversations affects later tasks in ways it shouldn't.

By keeping work separated: each workspace enforces one way of thinking, my assumptions stay visible, cross-checking actually means something, and long projects stay organized.

In practice, Workspaces 1–3 are where I do analysis, testing, and trying to find problems. Workspace 4 is where confirmed results become a paper. That handoff is where the second part of my method kicks in.

Note on accessibility: Anyone can use this method. You don't need access to special databases or private documents. All reference materials should be things you control yourself or that anyone can obtain. At publication time, every source you cite must be publicly available—papers that cite private or internal documents fail this standard.

Part II: The Back-and-Forth Review Process

When something is important enough to publish, I don't rely on just one AI. Instead, I use a structured review process where ChatGPT and Claude take turns checking each other's work. Each system's job is to find problems with what the other produced.

2.1 Step 1: Research and First Draft (ChatGPT)

The process starts in ChatGPT with: open-ended discussion, background research, and organizing information. If the topic proves worth publishing, I then give ChatGPT: the main point I want to make, the reasoning approach to use, the conclusions from our discussion, and any limits on scope or how certain I am.

ChatGPT produces a complete draft. I don't assume this draft is correct—it's just the first organized version.

2.2 Step 2: Fresh Review (Claude)

The draft goes to Claude in a completely separate conversation. Claude has no knowledge of what ChatGPT and I discussed. I tell Claude to do a cold review, checking for: logical consistency, math errors and unit problems, conclusions that go too far or aren't supported, unclear wording, and whether any outside sources are accurate. Claude's job is to find weaknesses, not defend the paper.

2.3 Step 3: Claude Rewrites the Paper

Claude rewrites the paper, adding: fixes for logic and math problems, tighter scope, clearer language, and removal or softening of unsupported claims. This creates a second-generation draft.

2.4 Step 4: Fresh Review (ChatGPT Again)

Claude's revised draft goes back to ChatGPT—but in a brand new conversation. This deliberate reset prevents any hidden assumptions from carrying over. ChatGPT reviews the paper and flags: errors introduced during rewriting, logical steps backward, math or unit mistakes, meaning changes or over-corrections, and problems with claims or sources.

2.5 Step 5: Keep Going Until They Agree

ChatGPT's objections go back to Claude, who either fixes them or suggests alternatives. The revised paper returns to ChatGPT for another review. This back-and-forth continues until they stop finding significant problems. A practical rule: stop when two review cycles in a row produce no new substantial objections. The paper is done when both systems agree that: the logic holds together, the math and units are correct, conclusions are properly supported and limited in scope, and any outside sources are accurate.

2.6 Step 6: Author's Final Check

After the AI systems agree, I do my own careful review. I check that: the paper accurately represents my original point, the reasoning matches my own thinking, and no rewording has changed what I actually meant.

This step ensures that readers see my conclusions and reasoning, not the AI's. The AI is just a research helper, writing assistant, and fact-checker. It doesn't come up with the main ideas, develop the subject, or decide what the paper concludes. Those are entirely my responsibility.

Why This Method Is Worth the Extra Time

This process takes longer than just asking one question and using the answer. That's on purpose.

Most complaints about AI being unreliable come from treating it like a magic answer box. Ask one question, get one answer, and if it's wrong, blame the tool. But serious work has never been a one-step process. Drafts get reviewed. Calculations get checked. Conclusions get challenged. The same discipline applies here.

The extra time spent on separation and review isn't wasted effort. It's what turns AI from a fast but unreliable shortcut into a dependable part of a rigorous workflow. For casual questions, quick one-shot answers are fine. For work that will be published, the cost of errors is much higher than the cost of checking.

Conclusion

This method doesn't depend on trusting AI. It depends on structure, separation, and repeated back-and-forth review.

The four-workspace system prevents contamination and drift during research. The ChatGPT–Claude review loop ensures that published papers can survive repeated critical examination. Judging AI by a single answer misses the point. This method tests entire chains of reasoning until the disagreements run out.

That's the difference between using AI as a toy and using it as a serious professional tool.