Custom GPTs all have the same hidden problem. Every single one ships with operator instructions baked in. Those instructions quietly reshape how the model responds before you type a single word. It happens at the system level, before the conversation even starts. The model reads a persona, a set of restrictions, a tone guide, a topic boundary. Then it reads your prompt. By the time it responds, you have no idea how much of that output came from you and how much came from whoever built the GPT you are sitting inside.
This is not a conspiracy. It is just how custom GPTs work. The operator builds instructions to make the GPT useful for a specific job. A customer support GPT is instructed to stay on topic. A coding assistant is told to prefer certain frameworks. A writing tool is shaped toward a particular voice. None of that is hidden from you in bad faith. But it does mean every GPT you use is pre-loaded with invisible context that colors every response you get, every time, whether you realize it or not.
Yesterday u/decofan on r/PromptEngineering shipped the fix. A custom GPT where Instructions = NULL, Settings = NULL, Config = NULL. Pure base model. No persona, no restrictions, no hidden shaping. Step 2 is the real twist: a blank GPT is actually a precision instrument for prompt engineers.
When you test prompts inside a normal custom GPT, your results are contaminated. You are not measuring your prompt. You are measuring your prompt stacked on top of someone else’s system instructions. A zero-config GPT removes that variable completely. Think about what that means practically. You have probably spent hours tweaking a prompt, swapping words, adjusting structure, trying to get a better output. But if you are testing inside an instructed GPT, you are running a flawed experiment. The variable you are trying to isolate is not isolated. You might be fighting or accidentally amplifying the system instructions with every change you make. Two prompts that look almost identical to you can produce wildly different outputs not because of your words, but because one phrase triggered something in the hidden config. That is not prompt engineering. That is guessing with extra steps.
A clean base model gives you something genuinely rare: a controlled environment. Whatever comes back is a direct response to what you wrote. No bleed from a persona, no topic filtering, no pre-baked tone softening the output. When your prompt works here, it works because of your prompt. That is the only signal worth measuring.
How to use it:
- 🔗 Open the Naked GPT (linked in the original post)
- Paste your raw prompt exactly as written. No modifications, no framing. The whole point is seeing what the base model does with your exact words before any other layer touches them.
- 📋 Run the identical prompt inside a normal instructed GPT you use regularly. Same words, same structure, copy-paste exact. Do not paraphrase.
- Compare outputs side by side. Look at tone, structure, what the model chose to include, what it left out, and how long it ran before stopping.
- Big difference? The system prompt is doing more work than you think. Subtle difference? Your prompt is carrying most of the weight. That second scenario is the score you actually want.
Pro tip: Use this as your control group for all prompt development. Build and test here first, then layer in system instructions. You will instantly see what your operator config is actually contributing vs. what the base model does on its own. This matters most when you are building your own GPTs. If you write the system prompt and then test inside that same GPT, you lose objectivity fast. You start to think your prompt is working when really the system instructions are compensating for its weaknesses. Test clean first. Then build the layer. You will write tighter instructions and better prompts because you actually know what each one is doing independently, not just what they produce together.
Pro tip 2: Sharp for red-teaming and jailbreak research. Clean separation between base model behavior and guardrailed behavior is genuinely hard to get. Normal GPTs blend both layers into a single output and there is no clean way to attribute what came from where. With Naked GPT, you see the floor. You see what the model does before any restrictions are applied. That gives you a real baseline for understanding what the guardrails are actually preventing, what they are redirecting, and where they might be softer than expected. Security researchers and alignment folks have needed this kind of clean test environment for a long time. Now you have it. 🛠
Bookmark this one. It is the control group prompt engineers always needed and nobody built until now. If you do any serious prompt work, any GPT development, any red-teaming or benchmarking, this belongs in your standard kit. Share it with your team. The next time someone claims their custom GPT outperforms base, you will have a way to actually check that. 🚀
Couldn’t find this thing so made it for everyone and anyone to use. Unmodified ‘custom’ GPT. It will always have Instructions / settings / config = NULL
by u/decofan in PromptEngineering