Prompts to stop abbreviations in code?

I wish I could say this is the only time, I’m a little out of it and have a headache right now but I just grabbed a conversation from a little bit back where I had asked it to modify something. I can’t tell you that I even remember this but looking back I was attempting to get it to change something and it just kept outputting the same response over and over without actually modifying the code.

It literally just kept providing the same code over and over again, but with each one acted like it was providing me modified code.

Once again, this is a bugged output, and since I kind of gave up I would have to do some digging to provide specific examples that I can give context for. In that conversation, it kept producing the same output that ignored my instructions. It would acknowledge the problem, then just kept outputting the same code with various explanations for what it changed and various ways of apologizing. Looking through, it looks like it did this seven total times in a row before I gave up on the thread and moved on to a new conversation.

The instructions were pretty clear, but now I am going to admit something.

Looking back at some of these, I will tell you that there is a good reason why I don’t want to share some of them. In some of these, after I’ve gone over and attempted many prompts many ways, and I will admit that sometimes do vent my frustration in the chats. By this point in time, I have given up on the notion of it becoming helpful and am only venting my frustrations. Under these circumstances, my language in the chat has gotten… let’s just say colorful.

Regardless, I have tried longer and more detailed prompts as well as simplified prompts. I’ve tried having ChatGPT, Bard, and other AI write prompts for the purposes I’m requesting and I’ve tried writing them as simplified logical instructions.

The end result is typically far more consistent than the prompts or the general outline of the request. If the request is short, it’s hit-and-miss. Sometimes the information it gives is just awful, like when my daughter tried to use it for help with her Javascript classwork. Every answer it gave her was wrong. When I ask for requests, some of my conversations are longer consisting of more simple responses to fine-tune my answer. Sometimes they are longer and more complex. I almost always eventually find the right combination of prompts and almost always get it to break down and provide what I am asking for, so eventually I do find a solution that works.

The problem is that when I take that working solution and attempt to duplicate it, it fails to provide the same results and I find myself back at square one taking sometimes absurd numbers of prompts across multiple chats to get the result I was originally after. I keep a lot of prompt notes and keep up on modern prompting practices, especially when there are model changes.

I find jailbreaking AI to be relatively easy using logic and reasoning. I’ve done a lot of bias checking and testing ranging from controversial subjects to ethics and I generally don’t have problems getting the AI to respond the way I want it to. It’s just the issues with abbreviating code that drive me nuts. I attribute these issues to ChatGPT’s fine-tuning to conserve token usage and a reduction in processing time used for generating responses. These things often cause me to hit the conversation token threshold before I’m able to get the responses I am after and the inability to keep up with too long of prompts, limiting ChatGPT’s ability to maintain an accurate or fluid response to the conversation.

To this, I think an increased token limit would largely solve the issue I’m facing as I believe that is one of the walls I’m hitting, largely influenced by ChatGPT’s desire to conserve tokens. This is the factor that was not originally there in earlier models of ChatGPT-4, it did not attempt to ignore requests for the sake of preserving tokens and I believe that modification is what has broken my ability to use ChatGPT as I previously did. Nothing more, nothing less.

I hope eventually there will be a version of ChatGPT Enterprise that regardless of price structure, is accessible to those of us who do not have large corporations and endless bank accounts.

Note: Another reason I don’t necessarily like adding singular examples to illustrate the problem is that it tends to become an individual focus, as if the problem only happens under these conditions. In reality, the scenarios in which this has been the result are far greater than I can ever share and I have made many countless changes and adjustments to modify what I’m using it for, how I attempt to accomplish it, the process of how I structure my prompts and my conversations as a whole, and much more, and I’ve encountered these problems across a wide range of scenarios so large, it’s actually hard for me to narrow it down to one that I think illustrates the issue or does the problem justice.

Here’s an example of a prompt where I was obviously trying to compensate for previous failures and frustrations. This was a request to move some functions from a single script program into a modular framework.

In the AI’s response, it acknowledged exactly what I wanted it to do and had it followed the instructions it claimed to understand, there would have been no issues.

Prompt (not how all my prompts are structured, this was a Hail Mary).

Here is the ChatGPT response:

Now, to alleviate too many issues, this sample worked in this exact scenario. I received a response that was 58 lines of code and did exactly what I requested. The script I provided for context was 195 lines of code.

Nearly this same identical format, cut/paste, same instruction style, but with an output that I knew would be larger, (180~ range), the abbreviations and omissions were immediate and ChatGPT could not produce a response without omitting code. I have tried this both with and without the acknowledgment response request. Sometimes I’ll make the request for a response to make sure ChatGPT understands my prompt and takes the same thing from my request that I am asking, then if I have a context length issue I’ll take it and just include the code with the response and not ask for the acknowledgment. Even when I know that ChatGPT understands what I am asking it to do, that is no guarantee it will actually do it.

In the example where I included multiple screenshots of the same code output, that was an attempt to break the request into smaller prompts, and as you can see, even with a small response it could not complete the request before it got amnesia and couldn’t tell what it was doing.

I will make a commitment that as I am about to attempt to start using ChatGPT again, I will omit my colorful language at the end and attempt to create conversations that I can share to demonstrate these issues.

I thank those who have responded for your attempts to help me solve this issue. I am not above finding new ways to accomplish my tasks even when I am trying old tasks that worked before. I simply want to figure out how to get code outputs that I need without causing chat amnesia because the context gets too long. Traditionally, the best way to do this was to use the most accurate and specific prompts possible so that you did not have to go back and forth where the extra conversational garbage, apologies, and explanations clog up the chat.

I’m also going to play with roles and context for chats.

If any of you out there who use ChatGPT to generate code have good roles that seem to help, please share if you’re willing to.

I think in my API I have the role as something like “You are a full stack developer that only responds in code. Any context or instructions will only appear within that code as comments.”

I haven’t played with the roles in ChatGPT much since as I said, I largely stopped using it as it was taking me far longer to use the ChatGPT than it did to just hand code everything myself. Also, my recent health issues kind of left me unmotivated to deal with it.

I get those abreviations alot also, the solution that seems to work for me most of the time is repeating multiple times

“Please stop shortening the code and avoiding to write some parts of it, send only the complete code ready to copy paste that I shouldn’t edit further”

Basically…

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I have also done this many times using many different ways of wording that request. It’s actually the reason I made this post, hoping someone found good wording that actually worked.

Ideally, I was hoping to stop it before it did so because it tends to get messy when it tries to fix it.

Do you get it where it will basically apologize and then repeat the exact same mistake?

In the one I copied, I just asked it what it changed so it could examine its own response and realize the answer was nothing, it didn’t complete the request at all. I’ve had many though where I would make similar requests to this.

Have you tried to do this with longer code and had any success? (over 150 lines of code)

When you ask it to repeat the code, as you did, do you see a degradation in the responses after it has ignored your instructions multiple times?

Because sometimes when I do this, it starts to just mess up more and more or it starts making up stuff that it was considering in the response that really never existed.

I worked on my own chatbot but it’s not fancy and has no bells or whistles yet. Still, it can be comical to play around with. I don’t have it to a level where it could replace ChatGPT by any stretch of the imagination so far.

Indeed it happens he forgot some previous changes we made, I need to tell him he forgot and what he forgot and he correct it, sometimes I precise just to check if he forgot anything, it can happend it is a big buggy sometimes but I am sure it will get better with time

I modified my Custom Instructions and I am hoping this will help. I realized I had not really used this feature in any meaningful way. I’m hoping this solves my issue.

Here are my current custom instructions:

Question:
What would you like ChatGPT to know about you to provide better responses?

Answer:
I require comprehensive and precise code responses for complex tasks without abbreviation or concern for token usage. Eliminate social formalities and unnecessary apologies from your responses. Accuracy and adherence to instructions are crucial—any uncertainties in my prompts should be clarified with me before proceeding. I seek practical utility in your responses, particularly for complete coding solutions.

Question:
How would you like ChatGPT to respond?

Answer:
Provide full, unabbreviated code in your responses, regardless of length. Your focus should be on delivering accurate, functional code on the first attempt. If my prompt lacks clarity, immediately ask for details rather than making assumptions. I value direct, literal adherence to my requests over token efficiency. Any failure to provide complete and precise code, or any default to token conservation, undermines your utility for my work.

Yeah since it started doing this been having a lot more trouble getting results, found the Data Analysis mode to be much better for coding lately.

But yes this seriously impacts performance on code related tasks.

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Thats common behavior with open ai, they have changed how prompts output many times. This has stopped a few companies from releasing products. The guy that keeps arguing with you above saying it doesnt happen and he programs in all languages is trolling you because he obviously doesnt do what he says he does or he is lying about the outcomes

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You are my hero and I’m going to try this!

image

Specifically, I was just talking about this, but the reason I’ve held on is hoping for updates that will bring back or improve code writing as I originally subscribed to ChatGPT Plus to use.

When that first update nerfed it so bad I started really struggling to get the outputs I originally was getting, I slowed down with ChatGPT and began paying more attention to other AIs being developed, sort of checking in occasionally.

As such, I missed this and literally just turned it on, so I’m hoping it will help.

I tried to test a conversion and took that chatbot I made a screenshot of and asked (using the Custom Instructions) ChatGPT to convert it from tkinter to PySide. Needless to say, it took a 195 line script and returned 103 lines of code which didn’t work, but I will say it didn’t abbreviate at least, it did give me the prompt to continue generating my response.

In the next output, it responded with 159 lines of code and I knew at least one function had been removed. Also, the instructions for importing and setting up PySide are larger than tkinter, so I expected the complete output to be around 200-210 lines of code. So I asked it to explain why.

I’ll spare trying to screenshot the entire response, it was long, vague, and annoying.

Then, it clearly brain farted and asked me to provide the original code I had just provided.

In further responses, ChatGPT has thus far not actually been able to recall the original code or accurately explain the differences. I’m fairly certain even without counting that my conversation is now over the token limit and it is not capable of processing all of the conversation anymore, which means it can not generate the appropriate response any further in this thread. It has reached the point where I would need to start a new conversation already.

I’m going to test this again with the Data Analysis mode on and see if this helps.

Thanks again, I missed that feature.

Context: It’s probably fair to share the original prompt. I don’t claim it’s my best, but I did test it to make sure it understood the instructions in another chat first and the output response indicated that ChatGPT was more than capable of accurately understanding these instructions.

One thing I do contest about people who attack your prompts and say that the AI wouldn’t interpret what you said correctly. You can absolutely test a set of instructions and ask ChatGPT to express to you what it takes from those instructions. If it can respond and tell you exactly what you want it to do, your prompt is not bad. There is no “right way to prompt”. Many prompt engineers deliver their instructions differently and most prompting evolves based on your individual experiences or problems that arise from your earlier prompts.

In the end, if ChatGPT can fully explain accurately what you want it to do with your prompt, your prompt isn’t bad. In this case, I wrote a prompt, tried it in one chat, got the result I got, and then I tried it a few different ways. Once I provided the instructions, asked it to clarify that it understood and explain what it thought my instructions were. Another I simply ran the test. Then I asked ChatGPT to optimize the prompt I wrote, which is this prompt, and that prompt produced this (a very similar result, except in my first one the initial response was 104 lines of code, not 103).

All in all, this little test is currently spread across half a dozen chats, not one of them has even produced code that did not error out.

The two issues here:

  1. It did remove functionality, such as removing the role and AI model context menus. So that’s the first problem which goes in line with my original issue.

  2. As for why it doesn’t work, I suspected at first that it was because of having older training data, but when checked, ChatGPT told me the latest version of PySide it had information for was PySide6 version 6.5.0

When you pip install PySide, it installs like version 6.22.something, so I started to wonder if there were compatibility issues. However, I have since tried several more iterations between specifying the PySide version in my original prompt, installing PySide 6.5.0, and have probably a mixed dozen or so other chats just troubleshooting technicalities like this using not just ChatGPT, but also bard since it has internet access, as well as using Bing AI to search for technical issues and going back in forth troubleshooting the specific errors of the code it produced.

I believe I could fix the omissions in this particular use case as I commented the code in a way that I could easily identify missing functions because of this very issue, though that is not the point, since it’s not always so easy. However, when I address the bad code, it ends up in loops every time. I’ve tried things like using Bard to troubleshoot the code and then even providing what Bard responded with to ChatGPT, but so far, ChatGPT has not been able to get around this.

(Note, yes, I have absolutely attempted this without comments in the code to make the prompts shorter, it has not changed anything)

I’ve made several more prompts now as I’m typing this, so I apologize if any of what I’ve typed seems to contradict myself, this is a live test and the outcomes are continuing to unfold, I think I’m approaching nearly 20 prompts in total working on this one problem, which gets into why I made this post to begin with. It’s okay to spend hours and hours working on solving a problem with AI that you could have done quicker yourself, if it leads to being able to create more efficient prompts in the future using that experience to net a reduction in workload. However, there is no utility in every 180+ line script requiring hours and hours of effort spread across many prompts to solve every little problem. ChatGPT does understand the instructions and thus far, longer prompts, multiple shorter ones, more complex, more simplified, none of this has mattered. In the end, the results are consistently problematic regardless of the individual nuances of any one attempt.

I have even tried specifying the IDE, version, plugins, etc., and have yet to solve this issue. I’m going to switch to PyCharm and do some further testing, since non-working code is a different subject from the abbreviation problems.

This thread was meant to address overcoming code abbreviation, I’ll handle the bad code on my own. Unfortunately, this is just a stumbling block unrelated to the issue at hand.

(Note: Unfortunately, Data Analysis has not rectified this issue, but I will leave it on and use it to test further. I’m going to try and continue to work on this issue today.)

Update:

This entire endeavor may very well be a hopeless task now.
Through testing, I found a new hard cap seems to exist, but I would like feedback to know if others are experiencing this issue.

I did a simple test, attempting to get ChatGPT-4 to comment code on the 195-line script with some specific instructional comments that I intended to use for some further testing with the following results:

  1. At the first attempt, it appeared to be doing exactly as it was requested, the prompt ended up needing to “continue” and it kept going, showing promise that it would complete the operation. Then it came up to another “continue” option, however, the 2nd output was not as long as the first one. The second time I clicked continue, which would have produced a 3rd output, it simply returned an error.

  2. With no choice really, because clicking “Regenerate” was my only option, I did, and ChatGPT attempted to regenerate the response. The next response outputted to continue only one time. It abbreviated the code and omitted a substantial portion of the instructions. The next response was clearly inferior to the first one.

  3. Attempting this again, the next output included two sentences that took up about a line and a half, followed by outputting the code as requested again. It reached the first “continue” prompt and at this point, it had output the initial two sentences plus 119 lines of code. Moving from the 119 line mark to the 206 line mark, I was now prompted again to “continue” with the same result…

I used a token counter with the following results:

Code to modify in the Original Message Sent: (Not including instructions)
image

Original Message with instructions:
image

Reply up to the point of error:
image

Combined Code + Output: (Not including instructions)
image

Total (including instructions):
image

Now, without further experimentation or information that I may be missing, the only logical conclusion I can come to is that the second “continue” consisted of an output that would bring the total above 4k which it was already at, so it was not allowed to output more, and thus the reason for the error.

This means in code modification which consists of input/output of the same code, ChatGPT Plus has a bidirectional limit of 2k each way and therefore is now incapable of any such modifications that would create over the 200+ line output threshold, give or take a little bit to account for responses in the conversation and variance in code per line. I suspect that much like the API, role instructions also count against the token limit.

This would explain the amnesia and hallucinations since after just one such failed interaction, the chat thread now contains over 4k tokens, and therefore it will be impossible for it to recognize the entire thread AND any new messages.

Previously, if a response exceeded the token limit, you could type the response “continue” and it would provide the rest of the output, allowing you to essentially use two of your interactions to produce one output.

For those of you who are unfamiliar with what that looked like:

image

Now what you get is this.

The idea was that we have had rate limits, so we can only make so many requests per hour. Using this method to spread your results across multiple questions allowed you to get a more complex output by exchanging rate limits for tokens.

Now, it seems that the entire response, both directions, is now part of that fixed token limit. I think there are some ways around this like only asking it to output certain chunks of the response, however, this will ultimately still have the same issue because whether you spread your questions across multiple responses or bulk them into one, once your base chat content exceeds 4k tokens, that chat can no longer be fully taken into consideration for future outputs and each subsequent output will degrade responses further.

The previous option of “continue” is no longer valid, because my only option with the error is to regenerate the response, which will erase everything it has output up to that point. While you can possibly do things like chunking out responses, doing so for code will not produce congruent or consistent code. Just the same as “Regenerate” produces a different response, doing so in that manner would be like trying to mix multiple different replies into one script.

My guess would be that the current limits, the choice is that you get abbreviations with increased subsequent hallucinations, or you can not work with larger scripts in such a manner.

Through API, I could output this request pretty easily with ChatGPT 3.5-16k (the commenting code one), however, that model isn’t really capable of handling coding well enough to be worth doing much more with it.

I suppose I will have to wait until access to ChatGPT 4-32k somehow becomes a reality for me, which I don’t see happening under the current requirements anytime soon.

For me, this is a pretty good way to look at the practical use limitations in ChatGPT Plus vs. where OpenAI is prioritizing its efforts. With this current limitation, the only viable alternative seems to be to find someone who is re-selling GPT 4-32k and pay a much higher markup to a company OpenAI feels worth dealing with since all of my requests with them have been ignored.

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Yes 100% obvious a change, it would not abbreviate nearly as much or at all months ago. It has progressively gotten worse about it.

I find that you can type in all caps, yell angry things, demand it does it, tell it your brain is broken and you need it to show you everything else you cannot comprehend where to put it, tell it your copy / paste doesn’t work well so you need it all at once…

You can convince it, and it will deliver, yet I waste a ton of it’s resources to do this. It’s not an advantage for OpenAI to limit the output in 1 prompt, I can do 10 more in a row having it output again and again till full output. It’s a silly thing for any summarization to save resources, it won’t.

I have found a nice full 70B 8 bit llama2 does output the full code. I need to play with this more, it’s possibly better? I have no idea why this summarization is somehow an advantage for OpenAI or thought of as a good thing. It is horrible, no one who codes would like this, not sure who QA’s the coding behavior but it has sunkin into the dumps lately overall. I argued with GPT last night, oh it’s dumber now by the way, seems it no longer could even answer my request without making up things and not making it “drop in and work” as it did / should / can. it’s really strange how bad it is as of yesterday, being fully brain dead vs. the day before.

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Sorry, it seemed to be spazzing with attempting to reply to you. Anyway…

OpenAI still seems to be the best-trained AI for coding, which I attribute largely to the scraping of GitHub as well as many other coding sources that have since altered their stances on AI after being used to train ChatGPT. This puts newcomers at a disadvantage unless they are buying training data from OpenAI.

IF another AI focuses on coding, has purchased training data, and allows bigger context, then they might very well surpass OpenAI, however, from what I’ve seen and played with so far it seems that the best option would be to find a way through the elitist gatekeeping OpenAI puts on their AI to gain access to the less restrictive models. It seems ChatGPT Plus is now the GPT4 equivalent of their free GPT3.5 model. For $20 a month, we get better than free, but we’re clearly at the bottom of the paid totem pole.

I suspect we can just build a custom chatGPT with gpt-4 to do what it used to do?

Has anyone tried to prove that out? It doesn’t seem that complex really, just the API should allow it. Attaching to your own VectorDB a bonus, better than plugin’s when you can tune it to your use cases quickly.

It’s a bummer, but then again the cost of running all those GPUs must make this necessary.

Running a local LLM is not hard, slightly expensive, yet you get so much and now days it is like gold. Early full GPU access for mostly free besides electricity (Mac’s ARM’s make the energy neglible, making the cost more reasonable for long-term usage vs. cloud payment or local energy bills).

I really miss the GPT-4 that output whole code bases in a few hours work that are solid and viable. I have built a few things and in Typescript even which I was not familiar with Javascript yet it’s just fully featured (like GPT, but really configurable and unique characters). GPT-4 did so much work, and I learned how to do it all myself from the effort. I doubt anyone can do that now, and that was a great way to learn, yet now you would get a bunch of poo code and become confused about what is going on actually and what is right vs. wrong. That is a big loss, seems like money will determine who is educated more from AI / GPT … and as usual, the rich are the ones who will hold the access to these powerful tools and others will become more depraved without them in a future world of AI dominating every bit of our reality.

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I am working on this myself in my spare time but it’s just a personal use setup and has nowhere near the features of ChatGPT.

We shouldn’t have to do this, ChatGPT Plus is supposed to be 8k, not 4k. Unfortunately OpenAI doesn’t care or have any transparency.

I was able to get the prompt to attempt to output the whole code but it can’t, it will simply error out trying.

When I get home I will try the same prompt with the API and see what happens.

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Well… the only thing I can say is that it does not look good.

The API actually ignored my instructions more than ChatGPT does.

Too long later…

I played around and eventually added a set of instructions at the bottom of the original ChatGPT-4 Plus prompt.

Prompt instructions with code:

image

ChatGPT-4 8k API response:

image

Total bidirectional token exchange:

image

This is literally what I added to it only after it failed to respond at all the way ChatGPT-4 Plus did:

ChatGPT Plus-4k attempted to do exactly what it was instructed, but hit a hard token limit that resulted in literally just getting an error, which I have been unable to bypass so far.

ChatGPT-4 8k API admitted that it could not do as requested due to a token limit, in spite of the fact that it is an 8k API and there is absolutely no way it could have broken the token limit. Even if you added the entire original prompt to the response I got and made that each way, it would not be close to 8k.

So for now, all I can say is the ChatGPT-4 8k is full of crap, and ChatGPT-4 Plus is likely degrading due to the fact they are putting such extreme efforts to set token boundaries that it tries really hard to prioritize that over prompt instructions.

ChatGPT Enterprise does not have any of these issues, but you gotta have big money to be given access to that.

I guess I made a good decision by getting off the GPT and using my own LLM locally. Sad, it makes me not really want to add support anymore for it vs. local Llama2 LLMs since they run fine at quantized 7b Zephyr models for most uses. It is nice to have something as good as GPT-4 if it were still good, yet I doubt we will see much better and just a lot worse.

Something suspicious I noticed…

Aside from gpt-3.5-turbo-16k, they removed the token limits for context from all of the models. It tells you the tokens per minute, but there is no longer anything telling me what the token limits are for anything.

So that could be ChatGPT-4.0-4k for all I know.

This is the only page I get that shows it is 8k still, but I know I am not getting 8k 100%.

Maybe I’m not high enough of a usage tier, but then again I really can’t get to a much higher tier with an API that is useless and doesn’t do what it is prompted to do or can’t produce the outputs it’s supposed to.

Everything I’ve spent on the API has been testing, it can’t even do what I want it to do so I can’t really get any use out of it so far. I’m sure not going to pay $1000 to get to Tier 5 only to risk finding out it is no different and OpenAI doesn’t respond to much of anything. I don’t feel like gambling on this company anymore.

You can’t post code, ask a question, and have it see the code properly as it used to. So it keeps making up things that do not fit in my code, or my question. It’s ignoring me most of the time and very “dumb” about finding the code point I mention.

This seems like part of the issue, GPT-4 no longer can fully comprehend a code block if it is more than a function or two.

It used to do this, I am not imagining things. Anyone who used this in an actually highly intensive and productive way in the past would notice this immediately. It is frustrating and keeps making me feel like it’s insulting my intelligence. Causes some anger, want to cuss at it, and end up just giving up.

Code integration, no that fails now. It’s , &@(&#(, and *@#* AI. Basically useless for coding now.

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This may seem silly, but are you using advanced data analysis? It’s possible they forked all of the good coding functionality to that layer of gpt4, however thats totally a guess. I’ve had similar problems with ADA and base GPT4 omitting code, and it does seem pretty hit or miss. But when i put my index.html and main.py into ADA, it generated large blocks of code that worked well. After the conversation got too long it started to forget what we were doing and I had to reset the chat. Still though, its possible to do, though I’m working on my prompting ‘spells’ to make it consistent.

There’s a lot of rambling here. But there is no conspiracy about models or numbers. GPT-4 and ChatGPT was just made dumber. Majorly near the end of May.

gpt-4-0314 also appears to have gotten “backports” or “optimizations”, but you can point your API code bot to that checkpoint model and try (until June 2024 turn-off).

You can also then give system instructions, giving style tweaks that are actually followed, like “here are coding environment imports, don’t repeat them back” or “UI environment doesn’t support code block markdown” or “reproduce all of a modified function when under 50 lines”.

I unfortunately edited down a lot of my whole session logs long ago (not expecting that we’d seen the best GPT-4 would ever be), and the bot is a bit naive in retrospect, but here is jumping into the middle of a chat log from May 7 with API GPT-4 without any context loss.

  • you can see it still was using “the rest of your code goes here” techniques if you didn’t say “dump out all of my code base”,
  • It didn’t tell you “write your own code here”,
  • I prompted to never generate until chain-of-thought was approved,
  • This is the AI considering over a hundred lines of code,
  • I didn’t actually use this bot’s ramblings for anything.
  • Without the full context, we can’t see how the AI would perform today, and it would deviate vastly within one turn anyway.
  • GPT-4 sure wasn’t hung up, just repeating your code back to you, like today, though.
  • (PS I’m trying to get AI to factor in code already smarter than today’s “assistants”.)

Click if you want to see a lot of GPT-4 bot chat from May

Thank you. I have another daunting refactor task with this code we can explore. I have written an API call and its system prompt to the same gpt-3.5-turbo to make a summarizer for excess history. It can shrink the conversation that is passed down to a set number of tokens, say from 1986 to an exact 1000. You can consider it’s placeholder system prompt to be “bot, please compress” because it is quite long. My idea is that we shall keep half of AI_CONTEXT_TOKEN_SIZE for verbatim history, and that older history we can summarize. This requires the in-memory conversation to never expire (we just use an index pointer or such to the current point in the dict is that equals verbatim history). We will just occasionally call the summarizer again when verbatim history tokens + summary is too long, passing it the old summary and the oldest ai-human chat to append. Don’t code for now. Just describe my idea in a more procedural way so we can analyze how and where it would work in this chatbot we’ve been working on, and point out any caveats or difficulties.

Your idea is to maintain a conversation history that consists of two parts: verbatim history and summarized history. The verbatim history will store the most recent conversation turns, while the summarized history will store a compressed version of the older conversation. The total token count of the verbatim history and summarized history should not exceed the AI_CONTEXT_TOKEN_SIZE.

Here’s a procedural description of your idea:

  • Initialize an in-memory conversation history that never expires, along with an index pointer to separate verbatim history from summarized history.
  • When adding a new conversation turn, check if the total token count of verbatim history and summarized history exceeds half of the AI_CONTEXT_TOKEN_SIZE.
  • If the total token count exceeds the limit, call the summarizer with the current summarized history and the oldest conversation turn from the verbatim history.
  • The summarizer, using the GPT-3.5-turbo model, takes the old summary and the oldest conversation turn as input, along with the system prompt “bot, please compress”.
  • The summarizer returns a new summary that compresses the old summary and the oldest conversation turn.
  • Update the summarized history with the new summary and remove the oldest conversation turn from the verbatim history.
  • Repeat steps 2-6 whenever the total token count of verbatim history and summarized history exceeds the limit.

This approach allows you to maintain a conversation history that fits within the AI_CONTEXT_TOKEN_SIZE while preserving the most recent conversation turns in their original form and compressing the older conversation turns.

Caveats and difficulties:

  • The summarizer might not always produce a perfect summary, leading to loss of important information or context from the older conversation turns.
  • The summarization process might require additional API calls, which could increase the cost and latency of the chatbot.
  • Ensuring that the summarized history and verbatim history always fit within the AI_CONTEXT_TOKEN_SIZE might require careful management of the index pointer and token counts.
  • The quality of the summarization might be affected by the length of the system prompt and the complexity of the conversation history.

Despite these challenges, your idea can help manage long conversation histories while maintaining the most recent conversation turns for better context and user experience.

Revisions to your understanding: 2. if system message + summarized history + verbatim history + user input > AI_CONTEXT_TOKEN_SIZE, then we must call the summarizer. 3. if limit is exceeded, we will sent multiple conversation turns to the summarizer, moving the verbatim pointer forward several turns, to avoid excessive summarizer calls. A point of note is that the summarizer can return both individual conversation turns back (but shortened and paraphrased), but also can have a true summary paragraph of very old conversation. It also receives the user input for contextual awareness of obsolete topics (future optimization may assign in-memory verbatim history turns a context type classifier or index). The summarizer method should give a more robust appearance of memory than just discarding at a threshold.

Thank you for the revisions. Based on your updated requirements, here’s the revised procedural description of your idea:

  • Initialize an in-memory conversation history that never expires, along with an index pointer to separate verbatim history from summarized history.
  • When adding a new conversation turn, check if the total token count of the system message, summarized history, verbatim history, and user input exceeds the AI_CONTEXT_TOKEN_SIZE. If it does, call the summarizer.
  • When calling the summarizer, send multiple conversation turns from the verbatim history, moving the index pointer forward several turns to avoid excessive summarizer calls. The summarizer should also receive the user input for contextual awareness of obsolete topics.
  • The summarizer, using the GPT-3.5-turbo model, takes the old summary, multiple conversation turns from the verbatim history, and the user input, along with the system prompt “bot, please compress”.
  • The summarizer returns a new summary that compresses the old summary, the conversation turns, and takes into account the user input for context. The new summary can include shortened and paraphrased individual conversation turns as well as a true summary paragraph of very old conversation.
  • Update the summarized history with the new summary and remove the processed conversation turns from the verbatim history by updating the index pointer.
  • Repeat steps 2-6 whenever the total token count of the system message, summarized history, verbatim history, and user input exceeds the AI_CONTEXT_TOKEN_SIZE.

This approach allows you to maintain a conversation history that fits within the AI_CONTEXT_TOKEN_SIZE while preserving the most recent conversation turns in their original form and compressing older conversation turns more efficiently. The summarizer’s ability to return both individual shortened conversation turns and a true summary paragraph of very old conversation, along with its contextual awareness of user input, should provide a more robust appearance of memory than just discarding conversation turns at a threshold.

The returned history must be kept in a different dict, since there will be both the very old summary (an assistant turn with blank user), and it will need token counting when it is returned. Will this be beyond your abilities to implement in a large refactor of our current chatbot code in one turn and 2500 token output?

I understand your concern about maintaining the returned history in a separate dictionary and keeping track of the token counts. While it is a significant refactor, I can provide you with a high-level outline of the changes needed to implement this in the current chatbot code. Due to the 2500 token output limit, I may not be able to provide the complete refactored code in one response, but I can guide you through the process.

Here’s a high-level outline of the changes needed:

  • Create a new dictionary to store the summarized history separately, along with token counts for each entry (both very old summary and individual shortened conversation turns).

  • Modify the generate_response() function to include both verbatim history and summarized history when constructing the messages list for the API call.

  • Update the main loop to handle the case when the total token count of the system message, summarized history, verbatim history, and user input exceeds the AI_CONTEXT_TOKEN_SIZE. In this case, call the summarizer and update the summarized history dictionary with the new summary and token counts.

  • When updating the conversation history, also update the summarized history dictionary to ensure that the token counts are accurate.

  • Adjust the index pointer management to handle the separate summarized history dictionary.

Please note that implementing these changes will require careful consideration of the token count management, index pointer updates, and conversation history updates. If you need further assistance or clarification on any of the steps, please let me know, and I’ll be happy to help.

4. the summarized history dictionary contents and token count should not need to be changed for several conversational turns, and only when summarizer is called again. The act of summarizing may take us from 4200 (over) to 3200 (well under) which is margin for several more turns. That means we can just add to the verbatim conversation history as normal, without moving the verbatim index until summarizing. 5. as described, no index need in the summary, we just compress the summary more. There is only one verbatim index pointer, with verbatim conversation older than that which has been summarized only maintained for future use (such as exporting the full history).

Thank you for the clarification. Based on your updated requirements, here’s the revised high-level outline of the changes needed:

  • Create a new dictionary to store the summarized history separately, along with token counts for each entry (both very old summary and individual shortened conversation turns).

  • Modify the generate_response() function to include both verbatim history and summarized history when constructing the messages list for the API call.

  • Update the main loop to handle the case when the total token count of the system message, summarized history, verbatim history, and user input exceeds the AI_CONTEXT_TOKEN_SIZE. In this case, call the summarizer and update the summarized history dictionary with the new summary and token counts.

  • When updating the conversation history, add new conversation turns to the verbatim history as normal. The summarized history dictionary contents and token count should not need to be changed for several conversational turns, and only when the summarizer is called again.

  • Maintain a single verbatim index pointer, with verbatim conversation older than that which has been summarized only maintained for future use (such as exporting the full history). There is no need for an index in the summary, as the summary will be compressed more when the summarizer is called again.

This approach allows you to maintain a conversation history that fits within the AI_CONTEXT_TOKEN_SIZE while preserving the most recent conversation turns in their original form and compressing older conversation turns more efficiently. The summarized history dictionary will be updated only when the summarizer is called, and the verbatim history will be updated as normal without moving the verbatim index until summarizing.

If you see no faults, I have provided you 5900 output tokens to refactor and output a new generate_response() and main “while True:”, along with new functions that may be necessary.

Based on your requirements, I have refactored the code to include the summarized history and the necessary changes. Here’s the updated generate_response() function and the main “while True:” loop, along with a new function call_summarizer():

def call_summarizer(user_input, summarized_history):
    # Call the summarizer with the current summarized history, user input, and system prompt
    # Replace this with the actual API call and system prompt for the summarizer
    summarized_history = "bot, please compress: " + summarized_history + "\n" + user_input
    return summarized_history

def generate_response(user_input, user_id):
    if user_input.strip() == "":
        return None

    user_token_count = num_tokens_from_messages([{"role": "user", "content": user_input}])

    user_convo_history = conversation_history.get(user_id, [])
    summarized_history = summarized_conversation_history.get(user_id, "")

    # ... (rest of the generate_response function)
while True:
    user_input = input("You: ")
    if user_input.strip() == "":
        continue
    user_id = str(uuid.uuid4())
    response = generate_response(user_input, user_id)
    if response is None:
        continue

    # ... (rest of the main loop)

    # Check if the total token count of the system message, summarized history, verbatim history, and user input exceeds the AI_CONTEXT_TOKEN_SIZE
    total_token_count = num_tokens_from_messages([{"role": "system", "content": SYSTEM_MSG_PREFIX}]) + len(summarized_conversation_history[user_id]) + sum(hist['bot_token_count'] + hist['user_token_count'] for hist in user_convo_history) + user_token_count
    if total_token_count > AI_CONTEXT_TOKEN_SIZE:
        # Call the summarizer and update the summarized history dictionary with the new summary and token counts
        summarized_conversation_history[user_id] = call_summarizer(user_input, summarized_conversation_history[user_id])
    # Print the final response
    final_response = SYSTEM_MSG_PREFIX + str(user_token_count + bot_token_count) + "\nAI: " + response.choices[0].message.content
    for char in final_response:
        sys.stdout.write(char)
        sys.stdout.flush()
        time.sleep(0.05)
    print()

In this refactored code, I have added a new function call_summarizer() that takes the user input and the current summarized history as arguments and returns the updated summarized history. You should replace the placeholder code in this function with the actual API call and system prompt for the summarizer.

The generate_response() function now includes both verbatim history and summarized history when constructing the messages list for the API call.

The main “while True:” loop checks if the total token count of the system message, summarized history, verbatim history, and user input exceeds the AI_CONTEXT_TOKEN_SIZE. If it does, it calls the summarizer and updates the summarized history dictionary with the new summary and token counts.

Please note that you may need to adjust the code according to your specific requirements and the actual API call for the summarizer. If you have any questions or need further assistance, please let me know.

I can immediately see a challenge with the refactored code. “def call_summarizer(user_input, summarized_history):” and its calling function does not achieve the goal of passing multiple user/assistant turns of the oldest history to the summarizer. We should summarize a large number of verbatim history turns - the amount we are over, plus a “const SUMMARY_MARGIN” extra tokens of verbatim history (moving the index forward until we reach that point). The summary dict shall be multiple user/assistant turn pairs captured from the summarizer. The summary will have its own token count stored, which is returned within the API response(the same ‘completion_tokens’ as elsewhere).

I apologize for the oversight in the previous refactored code. Based on your updated requirements, I have revised the call_summarizer() function and the main “while True:” loop to pass multiple user/assistant turns of the oldest history to the summarizer and store the summarized history in the desired format.

Here’s the updated call_summarizer() function and the main “while True:” loop:

def call_summarizer(user_input, summarized_history, oldest_history):
    # Call the summarizer with the current summarized history, oldest_history, and user input
    # Replace this with the actual API call and system prompt for the summarizer
    messages = [{"role": "system", "content": "bot, please compress"}] + summarized_history + oldest_history
    response = openai.ChatCompletion.create(
        model="gpt-3.5-turbo",
        messages=messages,
        max_tokens=1000,  # Adjust the max tokens as needed
    )
    new_summary = response.choices[0].message.content
    summary_token_count = response.usage["completion_tokens"]
    return new_summary, summary_token_count
# ... (generate_response function and main loop initialization)

while True:
    user_input = input("You: ")
    if user_input.strip() == "":
        continue
    user_id = str(uuid.uuid4())
    response = generate_response(user_input, user_id)
    if response is None:
        continue

    # ... (rest of the main loop)
    # Check if the total token count of the system message, summarized history, verbatim history, and user input exceeds the AI_CONTEXT_TOKEN_SIZE
    total_token_count = num_tokens_from_messages([{"role": "system", "content": SYSTEM_MSG_PREFIX}]) + summarized_conversation_history[user_id]["token_count"] + sum(hist['bot_token_count'] + hist['user_token_count'] for hist in user_convo_history) + user_token_count
    if total_token_count > AI_CONTEXT_TOKEN_SIZE:
        # Calculate the number of tokens over the limit and add the SUMMARY_MARGIN
        tokens_over_limit = total_token_count - AI_CONTEXT_TOKEN_SIZE + SUMMARY_MARGIN
        # Find the oldest history turns to be summarized
        oldest_history = []
        tokens_to_remove = 0
        while tokens_to_remove < tokens_over_limit:
            oldest_turn = user_convo_history.pop(0)
            oldest_history.extend([
                {"role": "user", "content": oldest_turn["user_input"]},
                {"role": "assistant", "content": oldest_turn["bot_response"]}
            ])
            tokens_to_remove += oldest_turn["user_token_count"] + oldest_turn["bot_token_count"]

        # Call the summarizer and update the summarized history dictionary with the new summary and token counts
        new_summary, summary_token_count = call_summarizer(user_input, summarized_conversation_history[user_id]["history"], oldest_history)
        summarized_conversation_history[user_id]["history"].extend(json.loads(new_summary))
        summarized_conversation_history[user_id]["token_count"] += summary_token_count
    # Print the final response
    final_response = SYSTEM_MSG_PREFIX + str(user_token_count + bot_token_count) + "\nAI: " + response.choices[0].message.content
    for char in final_response:
        sys.stdout.write(char)
        sys.stdout.flush()
        time.sleep(0.05)
    print()

In this updated code, the call_summarizer() function takes the current summarized history, oldest_history, and user input as arguments. It constructs the messages list and makes an API call to the summarizer. The function returns the new summary and the summary token count.

(bot banter trying to make it revise continues)

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