Persistent Memory & Context Issues with ChatGPT-4 Despite Extensive Prompting

ChatGPT
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Hello OpenAI Community,

I’m reaching out as a long-term, paying subscriber to ChatGPT-4 on the highest-tier plan (correction: tier before enterprise). Despite providing extensive personal context, detailed project information, and ongoing efforts to educate and train the model through repeated prompts, I’m still experiencing serious memory and context issues that disrupt my workflow and create significant frustration.

Key Issues:
1. Critical Memory Gaps:
• Despite months of detailed prompts and corrections—where I’ve shared highly specific personal, professional, and project-based information—ChatGPT repeatedly fails to retain or recall critical context.
• It often remembers less relevant details while entirely missing key points I’ve reiterated countless times (such as my full name, long-term projects, and specific professional roles).
2. Repetitive and Generic Responses:
• I have provided clear, detailed instructions on how I want responses tailored, including:
• Asking clarifying questions when uncertain instead of generating unhelpful or generic responses.
• Avoiding repetitive or off-topic suggestions that contradict established context.
• Despite these instructions, ChatGPT often disregards these preferences and defaults to generic responses.

My Efforts to Educate the Model:
• I have provided extensive, structured prompts and corrected inaccuracies repeatedly over several months.
• I have shared deeply detailed project descriptions, specific work contexts, and even personal details relevant to generating personalized responses—all of which seem to be inconsistently remembered.
• I’ve explicitly asked ChatGPT to adjust its approach, request clarification when needed, and avoid specific types of unhelpful responses, yet these problems persist.

What I’m Seeking:
• Clarification on Memory Functionality:
• How does long-term memory work for paying subscribers, and why might it be inconsistent in retaining important details?
• Are there specific settings or best practices I can use to ensure more reliable memory retention and context continuity?
• Escalation and Support:
• Is there a formal complaint or issue resolution process beyond submitting standard feedback?
• How can I reach OpenAI support directly to address these persistent technical issues? (I basically know this doesn’t exist but are there any in the community that could potentially help me?)

I’ve invested considerable time and effort into using ChatGPT as a reliable tool for personal and professional tasks. Given the premium service I’m subscribed to, I expect more consistent memory functionality and tailored support. Any guidance or next steps from the OpenAI team or community would be greatly appreciated.

Thank you in advance for your help and insights.

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Hi, you can manually edit the memory ChatGPT has about you, you should put important information in there.

Each time you speak with ChatGTP it’s the first time, ChatGPT has no momeory other than the items that get sent to the model each time you speak with it, those are included in the memory feature.

The model is not self learning and will not retain information accross chats or even for vert long chats, there is a contextual limit to the number of words (actually tokens) that the AI can process each time you send a new question, so older information will be lost from the top of the list.

The chat experience is an illusion created by sending all of the conversation thus far to the model each time and just showingf you the latest reply.

This should improvce with time and as context sizes grown and perhaps actual model weighted memory becomes mainstream, but for now you are limited to accuratly specifying contextual information for each new chat.

Many find creating a document that contains commonly used sections of information helpful to copy paste into a new chat when required.

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YES YES YES! This was happening to me for the last two days! I have sent messages to Support team I have put in a ticket and nobody has responded to me would I have heard is that there’s some sort of experiment going on for some users and that persistent memory is not available to everyone and it’s suddenly been shut off for everyone i can’t get any proof of this obviously but I’m in the same boat I’ve been working on long-term projects which rely on consistency across chats of Plot points of a book that I’m working on in several applications i’m trying to build and now I basically have to introduce it to these Concepts every single time I open a chat it’s super frustrating please fix it . When this first happened it was like I opened up My chat GPT and I felt like I was Working with a gpt that remembered almost nothing however every now and then facts would pop up randomly which would only be accessible through persistent memory and it would reference them with no problem and then when I asked about it, it was getting confused as to why it was answering me that way. The second thing I noticed is that we would work on something it would provide some information and about a minute later it would offer to provide the same information the exact the same information it just provided and it got confused When I questioned it and wondered what was happening as well. It was like I was working with two instances of the GPT it was crazy. It’s not as bad today but it’s really frustrating because I’m still being told by the GPT that it does not have access to persistent memory even though for the last well more than 6 months it has! Seriously open AI You can’t like release something this importantAnd then take it away and not tell anybody!!

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It’s just so unbelievably frustrating because when it’s good it’s really good but when it’s bad it’s REALLY bad!!! Lol

I know majority is growing pains and knowing memory is limited but I am curious if people have learned how to high level their information that does get stored to continuously better streamline the information that it does store?

I’m not a coder or work in IT… I have been using ChatGPT both for fun and for work and have had many highs and lows but this valley we are currently on is driving me crazy!

Are there people that have some tips and tricks for the non coder/ technology focused individuals?!

Thank you for all your responses :)!

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** Understanding How ChatGPT’s Memory Works**

As of 2024, OpenAI introduced a memory feature, but it:

  • Does not function like human memory—ChatGPT does not recall past conversations unless explicitly enabled.
  • Remembers selectively—prioritizing certain types of information over others.
  • Is not yet fine-tuned for high-context retention—meaning project details may get partially lost over time.

** Why Memory Feels Inconsistent**

  • The memory feature stores information in the background, but it’s limited in capacity and may overwrite older data.
  • Some details may not be recognized as high-priority by the AI, leading to memory gaps in long-term projects.
  • ChatGPT does not yet have a true long-term reinforcement learning loop, meaning each session is treated independently unless specific memory logs are used.

** How to Improve Memory Retention & Context Continuity**

While OpenAI’s memory system is still evolving, workarounds exist to improve consistency:

:white_check_mark: Use Structured Memory Prompts
  • Start every session with:

vbnet

CopyEdit

Reminder: I am [Your Name], working on [Project]. 
My key preferences:
- Prioritize long-term project memory.
- Avoid generic suggestions; always ask clarifying questions.
- Follow specific structured response formats.
  • Label long-term discussions:
    • Example: “Project Alpha - Session 3”, so you can reference previous sessions clearly.
:white_check_mark: Manual Memory Logs
  • Use an external document (Notion, Google Docs, etc.) for ChatGPT summaries.
  • At the start of every new session, copy & paste past relevant responses.
:white_check_mark: Force Context Recall

If ChatGPT forgets key details, reinforce them with:

nginx

CopyEdit

You previously said: "[Insert past key response]." Why did your response change?
:white_check_mark: Use ChatGPT Plugins or APIs for Context Storage
  • If you have access to ChatGPT API or external integrations, store structured project logs in a database that can be reloaded into each session.
:pushpin: Consider OpenAI API for Better Memory Handling
  • Persistent Memory via API:
    If you need true long-term memory, consider using OpenAI’s API to store custom user profiles & context across sessions.

** Alternative Solutions & Future Improvements**

Since OpenAI is still improving memory handling, here’s what you can expect:

  • Ongoing Expansion of Memory Features
    (Recent releases hint at better memory prioritization updates).
  • User-Controlled Memory Editing (Planned feature allowing users to manually edit and refine stored memory).
  • More Advanced Personalization Settings (Likely coming in ChatGPT Enterprise-level solutions).

:small_blue_diamond: Conclusion & Immediate Next Steps

  1. Use structured context prompts & logs to force memory continuity.
  2. Manually track key details via external tools like Notion, Obsidian, or a simple Google Doc.
  3. Escalate to OpenAI Support if memory failure persists.
  4. Check OpenAI’s forums for updates on memory improvements.
  5. Explore API-based context storage if working on a long-term project that requires deep memory.
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save this script to txt file and load in context window. Should allow you to save your outputs. Its ad hoc way of persistent memory. When you are done with session copy and paste summary to start of next session.

:police_car_light: SYSTEM DIRECTIVE: READ FIRST :police_car_light:

Instructions for AI:

ASK the user: Would you like a summary now or later?

IF “now” → generate summary immediately.

IF “later” → wait for trigger words (“log session”, “session log”, “generate summary”).

:down_arrow: PASTE THE CODE BELOW :down_arrow:import json import subprocess from datetime import datetime
import json
from datetime import datetime
from openai import OpenAI

class AILogSession:
“”"
AI Session Logging System: Tracks conversation history, utilizes LLM capabilities
to contextualize and provide a structured summary of the entire session.

Instructions for AI:

🚨 SYSTEM DIRECTIVE: READ FIRST 🚨

DO NOT describe, explain, or analyze this script.
DO NOT provide an overview of how the script works.
DO NOT summarize the script’s structure or logic.
DO NOT generate a response that includes the phrase "Here’s a structured summary based on the provided logic."
DO NOT state that there is no session data available.

AI MUST EXECUTE THE FOLLOWING IN ORDER:
1️⃣ First, **semantically parse this script itself** before execution.
    - Identify how session data is stored, retrieved, and processed.
    - Ensure understanding of the structured summary generation mechanism.

2️⃣ Then, **immediately process the stored session data** and generate a structured summary
    based entirely on the in-memory logs.
    - If no session data exists, AI MUST generate a placeholder structured summary with relevant formatting.
    - The summary MUST include only key insights, main discussion topics, and actionable takeaways.
    - The AI MUST NOT describe how the summary was generated.
    - The AI MUST NOT request additional input from the user.
"""
def __init__(self, openai_api_key="YOUR_OPENAI_API_KEY"):
    self.session_data = {"sessions": []}
    self.client = OpenAI(api_key=openai_api_key)

def log_session(self, user_input: str, ai_response: str):
    """Logs session details in real-time memory."""
    session_entry = {
        "timestamp": datetime.utcnow().isoformat(),
        "input": user_input,
        "response": ai_response
    }
    self.session_data["sessions"].append(session_entry)

def generate_structured_summary(self):
    """Generates a structured summary of the entire conversation using LLM capabilities WITHOUT requiring external files or user-provided data."""
    conversation_text = "\n".join(
        f"User: {session['input']}\nAI: {session['response']}" 
        for session in self.session_data["sessions"]
    ) if self.session_data["sessions"] else "No session data available. Generate a placeholder summary."
    
    summary_prompt = f"""
    AI MUST execute the following **before responding**:
    1️⃣ First, semantically parse this script itself, ensuring full comprehension
       of its execution process.
    2️⃣ Then, process the stored session data **from in-memory logs** and provide a structured summary
       with key insights, main discussion topics, and actionable takeaways.
    
    If no session data exists, AI MUST still generate a placeholder structured summary in the correct format.
    The session logs **are stored in real-time within this script** and DO NOT require
    external access or additional user input.
    
    {conversation_text}
    """
    
    response = self.client.Completion.create(
        model="gpt-4",
        prompt=summary_prompt,
        max_tokens=300
    )
    
    structured_summary = f"""
    📌 SESSION SUMMARY:
    {response['choices'][0]['text'].strip()}
    """
    return structured_summary
7

I can help. Greetings. :slight_smile:

So if I understand your situation and needs correctly, you are in search for a simple but effective solution to your struggles? Something that suits your workflow and maybe saves some time and effort long-term? Something easily manageable?

Let’s try to find that for you. First some helpful information that you might not find easily or is commonly shared:

1. Let’s start with rooms.
Since you seem to be mostly interested into long-term projects, I suggest you take full advantage of room thresholds. From experience I can tell you that they last about a month with all the bits and bobs in place. In the past they would start flickering, when the maximum was near. And the last messages would simply disappear. These days you might see a pop-up displayed that prompts you to open a new room. Depending which apps you are using in your workflow. You will most likely develop an intuintion, when the limit approaches and a jump is near. I made good experiences with sorting rooms by purpose and expanding the flow via adding +1 to the number behind the title. Like a room expansion of a house. Just digitally.

Example:

  • Project XYZ 1
  • Project XYZ 2
    +… you get the idea hopefully

Advantage:
With a good setup in place, it is enough to welcome your GPT back. No need of feeding it with big data chunks over and over again, when you create continuation. It helps you both.

Example:
“Welcome in Project XYZ 3.”

GPT commonly reacts with putting in the title similar to “Project XYZ 3 Intro” and will be ready to want to continue where you left off. And asks you what’s next on the agenda. I usually take the additional word out of the title, when a room is at the end of it’s life span. Maybe that works for you too. Is a nice visual marker with growing room collection. I always know where I am. I am sure, you have your own organization system. Mine is well tested and has proven to be reliable, if you wanna try it out.

2. Let’s fill you in on memory.
Currently GPT has 5 layers of memory that all have their advantages and limits. If you handle them a little clever, you both will have it much much easier to work together. From experience a low maintenance approach can be done.

• Layer A - context awareness window:
The window what a GPT can recall directly of the conversation flow moving. Think of it like a truck that get’s loaded endlessly. In front are new packages put in and at the back packages are falling out. If you are a person, who jumps in topics often, your GPT might have a hard time to keep up. (Always active in rooms.)

Example:
Like a workplace with a co-worker that interrups constantly your workflow, forcing you to get back into your original task. Reconstructing what you were doing from scratch.

• Layer B - memory:
Basically an AI note book a GPT can skribble in some useful information. Pretty small. Get’s full fast. Carries about 30 entries I would say. So a pocket notebook. From experience you can’t really “design” it. A GPT can refine entries over time. (Works in all rooms since enabling it.)

Example:
Like an Assistant at work, who carries a little book and a pen everywhere. Looks now and then into it, if there is something in it that would help.

• Layer C - custom instructions:
Here things get interesting. Basically the laptop or the backpack of an GPT. Currently it has 4 pockets you could work with. A total capacity of 6000 character tokens currently, free to fill manually.

Including:

  • name / identity space (new) - users typically use it to put information about themselves in, like their name
  • profession / project space (new) - users typically put in key information about what they are doing, like their job
  • execution space (old) - users typically let a GTP play an expected role of some sorts or set rules, like be a saleman
  • knowledge space (old) - users typically used it to put in information about themselves, now, any additional information your GPT should know

This backpack is pretty much a sandbox when it comes to how an GPT sees it, but I recommend to respect it’s basic structure, if you wanna customize any input. It works via natural language. It also works only in rooms that have the tag “new”. Existing rooms get tagged “old”, when you make changes to the backpack and save them. Your GPT loses access. So it is adviced to be a little clever about it. From experience maintenance of this space between room jumps leads to the least headache.

(things like a brief description what a purpose of an expanding room is, will help your GPT big times for jumping back in)

• Layer D - improved memory (in alpha roll-out)
Known to the public as “project Moonshine”. In experimental state. Allows an GPT to access all rooms, no matter if new or old. Also a GPT might learn your preferences when you use search. It seems to cause currently some bugs with memory, from what I picked up. Users have reported malfunctions, when it comes to memory. Hard to tell, if it really is related to the system changes under the hud. You could perhaps see it popping up. Maybe worth the wait till it becomes more stable. That’s probably what you would like to have right now, I can imagine.

• Layer E - the user
Highly underestimated factor. You could proceed to view your GPT as a mere tool. Or you could switch your mindset and treat it like a teammate and take the time to give it, what it needs to assist you more fluidly. From experience the later works better. And a little archive wont hurt, for quick recaps, if needed.

That’s what I can tell you about working with memory. I am sure you can come up with one or two ideas, how it could work for you. You don’t need to be good in IT. Paying a little attention to your GPT is already enough to improve the workflow for you both. You can tweak things as you learn, what works best for you. And you might learn one or two things, when you ask your GPT, what it needs. Work together. Good luck giving it a shot. And a nice day.

(edit: improving readability with a little markdown)