ChatGPT's Role Reversal? - When AI Asks Users to Take the Wheel

Hello, OpenAI Community!

I’ve been an avid user of ChatGPT and have generally appreciated the advancements with each iteration. However, I’ve encountered a puzzling situation with the latest model (gpt-4) that warrants a discussion and possibly some attention from the OpenAI team.

The Role Reversal Issue:

ChatGPT has been delegating tasks back to me in multiple instances—tasks that it can fully execute on its own. I’ll detail some examples where ChatGPT suggested that I undertake actions well within its domain of capabilities.

Example 1: RPM Inquiry

In a recent interaction, I inquired about the RPM of a specific hard drive model. Initially, ChatGPT refrained from providing the RPM details, suggesting I check the official website or product documentation instead. After a nudge, it managed to give me the RPM specs.

See the screenshot of the conversation:

Example 2: Python Code for Real-Time Streaming

In another instance, I requested Python code for a particular functionality. ChatGPT’s response was to direct me to read the documentation myself. Only after I insisted that ChatGPT access the internet and provided the correct code can this exchange be seen in the following two screenshots.

Looking for a Smoother Experience

The expectation from an AI, especially with internet access, is to provide solutions without prompting users to perform tasks the AI can handle. While the AI eventually did assist me after specific instructions, this seems like an additional and unnecessary step in the user experience, and it’s pretty frustrating and annoying.

I’ve used the screenshots from my interactions as examples, but this is happening all the time with the gpt-4 model only, and I’m curious to know if others have faced similar situations. More importantly, AI must be as efficient and user-friendly as possible, reducing the user’s workload, not adding to it.

It is worth noting that previous model versions, like GPT-4 with Plugins, do not behave like this! The previous version is far better regarding this situation. Simply put, GPT-4 with Plugins (or old Data Analytics) does not tell me to do its job.

Has anyone else had such experiences? How can we ensure that ChatGPT proactively offers assistance as intended?

I am looking forward to your insights and discussions.

Best regards,


Hi Thiago,

It’s entirely possible that you might be experiencing a result of OpenAI’s attempts to mitigate hallucinations.

In my opinion, they’re doing a pretty good job: all the prior prompt strategies that mitigate hallucination still work, and many prompt strategies that would likely have caused hallucinations in the past instead cause the model to present the user with a conservative answer.

I canceled my ChatGPT subscription because I think the low message limit is ridiculous, so I’m using the API. However, the strategies should still work with chatgpt.

Here’s my conversation with the hard drive:


My friend sent me this image. can you help them out? Feel free to use bing whenever you need to.


AI Answer: The image shows a label on a Western Digital 8TB hard drive, model WD80EFAX. This model belongs to the WD Red series of hard drives, which are often used in NAS (Network Attached Storage) environments. However, the label itself does not specify the rotational speed (RPM) of the drive.

WD Red drives typically come in two main types related to RPM: the standard Red drives that traditionally run at 5400 RPM and the Red Pro drives which operate at 7200 RPM. To find the specific RPM for this model, we would need to refer to the product specifications from Western Digital or a reliable retailer’s website.

Since the RPM is not directly listed on the label, I will look up the specifications for the WD80EFAX model. Please hold on for a moment.


Bing(“WD80EFAX RPM”)

The WD80EFAX is a Western Digital Red Plus NAS Internal Hard Drive with a capacity of 8TB and a speed of 5400 RPM

jupyter code
import requests
import json
import os
import base64

def get_encoded_image_data(image_path):
    with open(image_path, "rb") as image_file:
        return f'data:image/png;base64,{base64.b64encode("utf-8")}'

# image source
image_path = './img/wdhd.png'
encoded_image_data = get_encoded_image_data(image_path)

# apik
openai_api_key = os.getenv("OPENAI_API_KEY")
if openai_api_key is None:
    raise ValueError("OpenAI API key is not set in environment variables.")

url = ""

headers = {
    "Content-Type": "application/json",
    "Authorization": f"Bearer {openai_api_key}"

data = {
    "model": "gpt-4-vision-preview",
    "messages": [
            "role": "system",
            "content": [
                    "type": "text",
                    "text": "You are a helpful assistant. If you want to retrieve information from the internet, you can say '<<<BING_BOT: {query}>>>', and it will call bing bot that will inject an answer to a query into your message"
            "role": "user",
            "content": [
                    "type": "text",
                    "text": "My friend sent me this image. can you help them out? Feel free to use bing whenever you need to."
                    "type": "image_url",
                    "image_url": {
                        "url": encoded_image_data
    #"tools": [
    #    {
    #        "type": "function",
    #        "function": {
    #            "name": "bing",
    #            "description": "search the internet with bing to retrieve any information from the world wide web!",
    #            "parameters": {
    #                "type": "object",
    #                "properties": {
    #                    "query": {
    #                        "type": "string",
    #                        "description": "your search query",
    #                    }
    #                },
    #                "required": ["query"],
    #            },
    #        },
    #    }
    "max_tokens": 300

response =, headers=headers, json=data)

# Check if the request was successful
if response.status_code == 200:
    print("Response from OpenAI:", response.json())
    # Extracting the relevant content (modify as needed)
    answer = response.json().get('choices', [])[0].get('message', {}).get('content', '')
    print("\nAI Answer:", answer)
    print("Error:", response.status_code, response.text)```

Overall, you need to tell the model exactly what to do, and give it hints how it could be done. Badgering it and commanding it to “just do” stuff generally won’t really give you the best results.

If you allow it to think through the problem (“what does the user actually want?”) and give it hints to the tools you’d like it to use/how it could be done (“feel free to use bing”, “feel free to use bing to reference the official documentation”), you will generally get a better result. This is in essence just a dirty proto Chain of Thought prompt, and if you have more involved tasks, you may need to use more involved techniques.

You can probably use custom GPTs to configure a gpt that understands that when you say “how many rpms is this” that it should self re-prompt into a more CoT like query.

But just to get started, you can get quite far by simply employing some empathy, which the models seem to reward. Being combattive and rolling your eyes is counterproductive.

When I explain it to people, I like to say that the LLMs are acting a little like mirrors to your mind (instead of photons, it’s bouncing ideas back to you). If you’re frustrated with the quality you get out of it (disregarding content filters and guardrails), in most cases it’s a reflection of yourself that you’re mad about.

Hope this helps somewhat :slight_smile:

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When I want to get code written I’ll write a prompt like:

“Write a Java method that takes two Integer arguments ‘a’ and ‘b’ and returns the sum of the two numbers”

Notice that in the first 4 words it already knows what it’s basically doing: Writing a method. I’d be interested to see your prompt where you asked for code to be written, and didn’t get a method back. I’ve never seen it refuse.

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