Aloha!
I love Claude. Especially the Opus model. But it is a “measure once, cut twice” kind of system. It moves fast, generates brilliantly, and occasionally skips the part where it double-checks its own work.
That is why my most-used prompt has become:
“Are you sure? Double check everything and don’t make assumptions.”
Nine times out of ten, it catches something and corrects itself. That single sentence has saved me more hours than I can count.
The Problem with Trusting One Model
Here is the thing about AI in 2026: no single model gets everything right. Claude is exceptional at reasoning and writing; ChatGPT (and OpenAI’s Codex) tends to be more methodical about verification. They have different blind spots.
When I was building systems for clients, I would never rely on a single source of truth for anything critical. The same principle applies to AI. If one model generates the work, a different model should verify it.
This is not a theoretical idea. I set this up and use it every day.
For Technical Users: Claude Code + Codex CLI
If you are comfortable with the command line, the cleanest approach is to have Claude Code call out to OpenAI’s Codex CLI for verification. No browser needed; it stays entirely in your terminal workflow.
You do this by editing your CLAUDE.md file (the instructions file that Claude Code reads at the start of every session) to include a verification step. Something like:
“Whenever you complete research or generate a recommendation, verify all findings and assumptions through the Codex CLI. Pass the full context, get verification, and incorporate the feedback before presenting the final answer.”
Claude will then call Codex, pass the relevant context, get a second opinion, and merge the feedback into its answer. Two models, one workflow, better results.
For Everyone Else: Claude Cowork + Chrome
If you are less technical and using Claude Cowork (Claude’s browser-based assistant), there is an even simpler setup. Install the Chrome extension and tell Claude:
“Update the CLAUDE.md so that whenever you do research, you verify all of your findings through ChatGPT. Open ChatGPT in the browser, paste the context, read the response, and use that feedback to improve your final answer.”
That is it. Claude will open a browser tab, paste its findings into ChatGPT, read the verification, and fold it back into its work. It sounds wild. It works.
The Hidden Weapon: ChatGPT Deep Research
I know OpenAI has its complicated side. But from a pure utility standpoint, the $20/month subscription is absurdly good value. I am confident I burn more than that in electricity from how much I use it.
The “Deep Research” feature in ChatGPT is particularly powerful for market analysis and finding prospects. It is close to Perplexity Pro in quality. You can get Claude Cowork to use it, but you only get 10 deep research queries per month. Use them wisely and tell Claude to be patient while it runs (it takes 15 to 60 minutes per query).
Why This Matters for Leaders
This is not just a productivity hack. It is an operational principle.
Every system I build for clients follows the same pattern: generate, then verify. Create a backup, then test the restore. Write the policy, then check it against reality. The AI workflow is no different.
If you are leading a team that uses AI tools, the question is not “which model is best?” The question is: “what is our verification step?” Because the quiet failure of AI is not that it gets things wrong. It is that it gets things wrong confidently, and nobody catches it.
Key Takeaways
- No single AI model is infallible. Use one to generate, another to verify. Different models have different blind spots.
- Your most powerful prompt is a question. “Are you sure? Double check everything and don’t make assumptions.”
- For Claude Code users: edit your CLAUDE.md to include a Codex CLI verification step.
- For Cowork users: install the Chrome extension and instruct Claude to verify through ChatGPT’s web interface.
- Deep Research is underrated. ChatGPT’s research mode is excellent for market analysis and prospect research. Ten queries per month; spend them on things that matter.
- Verification is a leadership principle. Apply it to AI the same way you would apply it to backups, security controls, or financial reports.
Connect with me on LinkedIn, or book a conversation if you want to talk about building AI workflows that actually hold up under pressure.