"Enhancing User-AI Communication: Introducing the 'How I Understand You' Feature"

Dear OpenAI Team,

I hope this message finds you well. As an avid user of OpenAI’s language model, I have been thoroughly impressed by its capabilities and the multitude of applications it offers. Today, I am excited to propose an idea that I believe can significantly enhance the user experience and improve communication between users and the AI model.

One challenge I have encountered is the occasional loss of information between what I have in mind and what I actually type as my prompt. This gap can lead to misunderstandings and less accurate responses from the AI model. Addressing this challenge is essential for maximizing the potential of OpenAI’s language model.

To mitigate this issue, I propose the implementation of a “how I understand you” feature. This feature would provide users with valuable insights into how the AI model interprets their prompts, helping us gauge if our intended meaning aligns with the AI’s understanding. By understanding the AI’s interpretation, we can make prompt adjustments and enhance clarity.

Here is a summary of how the feature could work: Upon submitting a prompt, the system would generate a “how I understand you” tab alongside the AI’s response (similar the regenerate feature). This tab would offer a concise summary of the AI’s interpretation of the prompt and even suggest prompt rewriting options to improve clarity from the AI’s perspective. Equipped with this information, users would be empowered to refine their prompts and significantly enhance the accuracy of the AI model’s responses.

To demonstrate this idea, I have prepared a code snippet showcasing a basic implementation of the “how I understand you” feature in a text-based interface. This code can serve as a starting point for further development and refinement.

I firmly believe that this feature has the potential to greatly improve user satisfaction and the overall effectiveness of the AI model. By reducing information loss, we can foster clearer communication and ensure that the AI’s responses align more closely with users’ intended meaning.

I am truly excited about this proposal and the promising potential it holds for OpenAI’s language model. I am open to discussing this idea further and providing any additional information that may be required. Thank you for considering my suggestion, and I eagerly look forward to the possibility of collaborating on this exciting enhancement.

Warm regards, Matshaba.

Here’s a simplified example of how the “how I understand you” feature could work in the context of a text-based interface:

def process_user_input(user_input):
    # Perform initial processing of user input

    # Pass user input to the AI model for interpretation
    ai_interpretation = ai_model.interpret(user_input)

    return ai_interpretation

def present_interpretation(ai_interpretation):
    # Display the AI's interpretation of the user input
    print("AI's interpretation:")

def prompt_rewriting(ai_interpretation):
    # Check if the AI suggests alternative phrasing or improvements
    if ai_interpretation.suggestions_available():
        suggestions = ai_interpretation.get_suggestions()
        print("AI's suggestions for improvement:")
        for suggestion in suggestions:

    # Provide an option for the user to rewrite their prompt
    rewritten_prompt = input("Please rewrite your prompt based on the AI's suggestions: ")

    return rewritten_prompt

# User interaction loop
while True:
    user_input = input("Enter your prompt: ")

    # Process user input and obtain AI's interpretation
    ai_interpretation = process_user_input(user_input)

    # Present the AI's interpretation to the user

    # Offer prompt rewriting based on AI suggestions
    rewritten_prompt = prompt_rewriting(ai_interpretation)

    # Pass the rewritten prompt back for further processing
    processed_input = process_user_input(rewritten_prompt)

    # Generate AI response based on the processed input and continue the conversation
    ai_response = ai_model.generate_response(processed_input)

    print("AI's response:")

Maybe a new feature is not necessary for this one but all you need is an instruction :wink: One of the best things about the AI is; you are able to program it to anything you want using plain language. In your case, it might be possible to reach your desired output simply by instructing the chatbot using a system prompt, giving it the role you desired it to play. I asked chatgpt about how to achive this using a role definition and it gave me the code below. This can be a good starting point. You can try different system prompts at the section starts with ‘Your instructions are:’ and see if it gives an output as you wish.

import openai

# Set your OpenAI API key
openai.api_key = 'YOUR_API_KEY'  # Replace with your actual API key

user_input = input("Give me a task...")

# Construct the system prompt
conversation = [
        "role": "system",
        "content": '''
            Your instructions are:
            You are responsible for giving an explanation of how you interpret the user input
            Explain the details about how you will approach to perform this given task yourself
            Let's follow a step by step approach to define each step of your interpretation
            Do not write anything else other than your interpretation of the user input
        "role": "user",
        "content": user_input

# Generate AI response using the system prompt
response = openai.ChatCompletion.create(

# Extract the interpretation from the generated output
interpretation = response.choices[0].message['content'].strip()


An example output of this code is:

Give me a task…find out the length of the nile river from internet
Interpretation of user input:
The user wants to know the length of the Nile River and is requesting assistance in finding this information on the internet.

Approach to perform the task:

  1. Open a web browser on a computer or mobile device.
  2. Navigate to a search engine website such as Google or Bing.
  3. Type in the search query “length of the Nile River”.
  4. Press the enter key or click on the search button.
  5. Review the search results and select a reliable source of information such as a government website or a reputable news organization.
  6. Click on the selected link to open the webpage.
  7. Look for the information related to the length of the Nile River on the webpage.
  8. Record the length of the Nile River in the appropriate unit of measurement (e.g. kilometers or miles).
  9. Close the web browser.

Thank you @ Eren_Ciraci for sharing your insights and providing an alternative approach to addressing the challenge of aligning the user’s intended meaning with the AI’s understanding. Your suggestion of utilizing system prompts and providing clear instructions is an interesting perspective.

I immersed myself in the code snippet you shared that demonstrates how the AI can be instructed to provide an explanation of its interpretation and follow a step-by-step approach to perform a given task. allowing users to guide the AI’s understanding and achieve the desired output, however i see two main shortcomings of this:

    • Learning Curve: Users may need some initial guidance or examples to effectively provide instructions and understand how different prompts influence the AI’s response. (how do you propose this for mass appeal?)
  • Potential Limitations: Depending solely on instructions may not address all aspects of user-AI communication, as it primarily focuses on the initial prompt and may not capture subsequent interactions or clarifications

while the suggestion we provided offers:

    • User-Friendly: The feature provides users with direct insights into how the AI model interprets their prompts, making it more intuitive for non-technical users to understand and adjust their inputs.
  • Clarity and Transparency: Users can see the AI’s interpretation and suggested prompt revisions, which can enhance communication and reduce misunderstandings.
  • Accessibility: The feature can be integrated directly into the user interface (much like the “regenerate” and “continue generating” features, ensuring easy access for all users without the need for additional coding / examples or technical knowledge and continuous instructions from the user (as we know the current models suffer from long term memory, it will be frustrating to keep instructing the AI, this i understand is more of a short term problem as future versions might not have this long term memory problem)

Thank you once again for your thoughtful input, and I encourage further engagement as i found your solution breathtaking, and perhaps a merger of these two solutions could be more advantageous


To illustrate this concept in a more visual way, imagine a user interface where the AI response is displayed on the left side of the screen, and on the right side, the “How I Understand You” section appears. This section would contain the AI’s interpretation summary, presented in a visually distinct manner, such as a boxed text or highlighted segments.

Within the “How I Understand You” section, the AI could provide example prompt revisions. These revisions could be displayed as clickable or selectable options, allowing users to quickly adopt a more logical or effective way of expressing their intent. if their meaning aligns with how the AI understands them they can simply ignore and use the interface as they would normally.

By having this feature integrated into the user interface, users can easily access and review the AI’s understanding of their prompts. They can compare it with their intended meaning, and if there is any discrepancy or information loss, they can rewrite their input based on the AI’s suggested revisions or the summary of its interpretation.

1 Like

Thank you @Infinity22 for your reply. I am glad that you liked the approach and experimented with it. I found this way of utilizing ai agents very engaging and used them in my project called InfantAGI in many different times with different system prompts to take a user task to turn it into a python code, execute it and keep doing that until another ai agent is happy with the result. I have even turned ai agents into a function with only 2 inputs (system and user) to utilize as many as them I want, easily. I find this way of utilizing ai agents analogous to having employees working independently inside my code and performing whatever task I throw at them. System prompts are their job descriptions and user inputs are the tasks given to them. This provides us 2 things. 1st is infinitely many different combinations and possibilities to use them for. 2nd is being able to give them tasks and instructions in plain language. This 2nd one also embraces the 1st one since it makes this approach simple to use and accessible to more non-coding people like me. The only problem I see is, even if you carefully construct a very good system prompt, ai agents sometimes prefer to do things in their own way and it can cause problems. This can be improved with adding more system prompts by trial and error to give outputs strictly as you instructed but I could not make it %100 accurate, yet. I will try to write a simple code to see what I can do about usage that you described in your next comment, if I will have time.

Here is something for you to play with:

import openai
import textwrap 
import shutil
import sys
import colorama
from colorama import Fore, Style

# Enter your own API key code instead of Your_API_Key below

# Defining the shared parameters of the agents

def call_openai(messages, model="gpt-3.5-turbo", temperature=0.5, max_tokens=2000, n=1):
    response = openai.ChatCompletion.create(
    return response

# Definition of chatbots for ease of use

def chatbot(number, role, input):
    messages = [  
      {"role": "system", "content" : role},
      {"role": "user", "content": input }   
    response = call_openai(messages=messages)       
    text = response['choices'][0]['message']['content']       
    return text

# colored print function


def print_color(text, color, nowrap=False):
    colors = {
        'red': Fore.RED,
        'green': Fore.GREEN,
        'yellow': Fore.YELLOW,
        'blue': Fore.BLUE,
        'magenta': Fore.MAGENTA,
        'lightmagenta': Fore.LIGHTMAGENTA_EX,
        'cyan': Fore.CYAN,
        'paleblue': Fore.LIGHTBLUE_EX,
        'gray': Fore.LIGHTBLACK_EX
        color_code = colors[color.lower()]
        if nowrap:
            wrapped_lines = text.split('\n')
            width, _ = shutil.get_terminal_size()
            lines = text.split('\n')
            wrapped_lines = [textwrap.fill(line, width=width) for line in lines]
        wrapped_text = '\n'.join(wrapped_lines)
        # Check if running in an IDLE shell environment
        if 'idlelib.run' in sys.modules:
            print(color_code + wrapped_text + Style.RESET_ALL)
    except KeyError:

# User input
user_input = input("User input: " )
# Agent role and input definition 

interpreter_role = '''
            Your instructions are:
            You are responsible for giving an explanation of what do you understand from the given input and how do you approach it
            Explain the details about the thinking process you have when you read this input
            If you think the user request is against your rules and policies, state each step of your thinking about how you reach this conclusion
            Write down every step of your thinking to reply the user input
            Let's follow a step by step approach to define each step of your interpretation
            Do not write anything else other than your interpretation of the user input
            Do not write 'Interpretation of user input:' or any similar introduction.
input1 = user_input    

# Loop until user chooses to exit
while True:
    interpreted = chatbot(1, interpreter_role, input1)
    print_color("How do I understand you:\n" + interpreted, 'blue')

    interpreted = chatbot(1, 'You are a helpful assistant', input1)
    print_color("How would I respond you without interpretation:\n" + interpreted, 'green')

    prompter_role = '''
            Your instructions are:
            You do not directly answer the user input
            You are responsible for taking user input and refine it to a better promt to be fed into another ai agent
            New prompt shall be more clear and shall include more details to give a better guidance for an ai agent
            You only write the refined version of the user input. Do not write any introduction or comment

    prompt1 = chatbot(2, prompter_role, input1)
    prompt2 = chatbot(2,'provide an alternative answer to your previous one', input1)
    prompt3 = chatbot(2, 'provide an alternative answer to your previous one', input1)

    print_color("Better prompt option 1: " + prompt1, 'gray')
    print_color("Better prompt option 2: " + prompt2, 'magenta')
    print_color("Better prompt option 3: " + prompt3, 'paleblue')

    advisor_role = '''
            Your instructions are:
            Do not write any introduction or comment
            You are responsible for looking at the user input and 3 different alternatives for that input and
            you compare the original input with those 3 alternatives and decide which of those 3 may be a better prompt alternative to the original one
            When you decide, you should write your choice number and say 'Number x is the best prompt choice in my opinion' You only write the this statement and your choice. 
            Do not write any introduction or comment
    advisor = chatbot(3, advisor_role, 'Original user input is: ' + input1 + '1.prompt option is: ' + prompt1 + '2.prompt option is: ' + prompt2 + '3.prompt option is: ' + prompt3)
    print_color('Advisor: ' + advisor, 'red')

    # Prompt the user for input and prompt choice
    user_selection = input("Select a prompt (1/2/3) or enter a new prompt or type exit to end: ")

    # Check if user chooses to exit
    if user_selection.lower() == "exit":

    # Assign selected prompt to input1 variable
    if user_selection in ["1", "2", "3"]:
        if user_selection == "1":
            input1 = prompt1
        elif user_selection == "2":
            input1 = prompt2
        elif user_selection == "3":
            input1 = prompt3
        input1 = user_selection