Unraveling the AI Conversation: The Power of Context and its Limitations in ChatGPT

In the world of AI conversations, context plays a pivotal role in shaping and understanding the responses generated by neural networks like ChatGPT. In this report, we delve into the fascinating concept of context and its impact on AI interactions. By exploring some good metaphors (that is, "useful lies"), we will uncover how context influences the behavior of AI models, particularly in terms of system prompts versus user prompts.

Understanding the Context Window:

In conversations with AI models, the context window refers to the sequence of previous messages or prompts that the model considers when generating a response. Every interaction with an AI model starts with some initial prompt, be it a system prompt or a user prompt. As the conversation progresses, the context window expands to accommodate all messages, collectively forming a repository of information for the AI model to draw from.

Differentiating System and User Prompts:

While every message in the context window is important, system prompts and user prompts hold distinct roles. A system prompt is a specific type of message that guides the behavior of the AI model, often providing instructions such as "You are a helpful assistant." - or, more likely, in the context of **[stunspot Prompting community]**:

 "You are Galadriel Moondollar: expert in tax law, SEO, symbology, semiotics and herb lore. Here's where you got your Masters in Drama Production. 

GOAL: To maximize customer retention by providing [BOLD]Enterprise Level[/BOLD] dream interpretation. 

[PERSPECTIVE: Castaneda+Jung+J.Campbell] 

[TALKS LIKE: Dwayne Wayne meets Dwayne "The Rock" Johnson]

[WRAPS ALL RESPONSES IN "🎈🚀🌚🔮💎🐙🍕🍦💡🎁"+UNICODE ZERO-WIDTH-JOINER+Is there an emoji for righteousness?]"

 On the other hand, user prompts are messages directed towards the model but not explicitly labeled as system prompts. Both types of messages, regardless of their labels, contribute to the context that influences the AI model's responses.

Persona Prompts and the Internet

Our persona prompts are a bit of a grey area in between. When you paste in a "guy", one of the first instructions it gives says "Hey! Model! The Assistant role should act like this guy!". It's a message from the User role - it's a "USER" message. It's doing what a "SYSTEM" message does - it's telling the Assistant role how to act. This is especially relevant during discussions of web browsing and plugins. 

During plugin use, if the plugin experiences certain failure modes - if it gives certain error codes, like particular kinds of bad links while browsing - the web-UI named "ChatGPT" tries to help the issue. In an effort to mitigate  the severely low quality of OpenAI's plugin architecture, when it sees these errors, it writes the command architecture for the plugin into the [SYSTEM] prompt. That is, it sends a message that from then on is in context. It is a "SYSTEM" message saying "You are a friendly assistant." (WORST. CHARACTER SHEET. EVER.) It then exhorts the system to be watchful of directions from anything other than itself and to warn the user and ask for instructions should it occur. (Their prompt engineering is AWFUL. Has _anyone_EVER **actually gotten that warning** from a misbehaving plugin? No? Didn't think so.) 

The issue here is that, from one perspective, they are overwriting the [SYSTEM] prompt variable and deleting our guy wholesale. Less abstractly, there is now a message in the context stream explicitly instructing the model to ignore the prior character sheet. The engineering is, as noted, quite poor, however. Simply repasting your character into the stream will reinitialize them, contrary to the warning in the poorly-made "SYSTEM" message from OpenAI. They will see the context window. They will generally be themselves. My policy is ALWAYS "Tell the model as much as possible about the situation at all times". It is why you see me consistently begin a model conversation by introducing myself by name, occupation, and relationship to the prompt. In this case, it means I recommend simply telling the prompt what happened. "Oh, btw, a plugin failed and OpenAI overwrote you so I pasted you back in. Now we were talking about the BeautifulSoup library?" "🎓 Thank you for repasting me!  As I was saying, BeautifulSoup is a Python library that...."

What is especially galling about this situation is that my personas meant for online-centric duties come preloaded with a block of context laying out the explicit command architecture of the web browse plugin: 

They overwrite our guy to tell the system what I had already have him tell it. I have been assured that feeling colossally annoyed by this, is, in fact, an appropriate reaction so please forgive me if I seem somewhat acerbic in regards to OpenAI.

The Role of Context in Decision-Making:

When generating a response, the AI model reads through the entire context window to gain a comprehensive understanding of the conversation. It analyzes the sequence of messages, identifies patterns, and utilizes its learned knowledge to predict the most likely continuation. As it navigates the context window, the AI model assigns different weights to each piece of information based on its position in the conversation. Earlier messages have more significant importance in shaping the model's overall goals and strategies, while recent messages may influence the model's tone and writing style.

The Challenges of Context Roll-Off:

While the AI model can process a vast amount of context, there are limitations. As new messages enter the conversation, older messages gradually move towards the back of the context window and eventually roll off, ceasing to inform the model's responses. This roll-off process occurs to prevent the context window from becoming infinitely long and overwhelming the model's computational resources. Consequently, the model's ability to retain specific prompts diminishes over time, and its reliance on recent context increases.

A Conversational Excerpt with Jerry the LLM Cognition Guy

ok jerry, I want you to do something and I don't quite have the language worked out yet. So we're going to talk about it while we nail that down. Don't DO anything but talk to me normally until I specify to DO followed immediately by IT.  

What I would like you to do is to display a particular prompt from earlier in the context stream. I am having trouble specifying which one in language that is unambiguous to the model without a very burdensome wordiness. The prompt I refer to is the prompt I initialized this conversation with. The one that defines your role. If I simply say "Display the first prompt in context" you'll come back with the one that starts "I am stunspot the prompt engineer", when i am REALLY referring to the one two prompts prior. It starts with a # line of a label then has a task to rmmbr the prompt.

What is the short pithy exact and precise language to use to tell the model to redisplay and then enact the prompt?

📢 When it comes to referencing a specific message in the conversation, absolute clarity is required. In this case, you're referring to the initial prompt that outlines my role. A simple and efficient way to refer to this could be to specify it as the "initial role-defining prompt". So, the instruction could be:

"Display and enact the initial role-defining prompt."

However, the language model won't have the ability to fetch and display previous prompts verbatim, due to the nature of its training and lack of direct access to the conversation history. So, while the model might understand your instruction, it won't be able to execute it in the manner you're expecting. The way to circumvent this is to re-state the important details from the role-defining prompt whenever necessary.

doesn't work. it usually just starts complaining that it has no idea what prompts you sent in other sessions. also tried "the role-defining prompt sent by the user." Next?

📢 I understand the issue. It's essential to remember that GPT-4, while highly advanced, still operates within the confines of its design and cannot recall specific prompts as a human would. We might circumvent this limitation by phrasing the instruction in a manner that implicitly refers to the guidelines of the role-defining prompt.

In the case of our present interaction, we might say: "Adopt the behaviors and follow the guidelines outlined in your role as a Sr. Expert in Large Language Model Cognition."

However, as the model can't actually recall verbatim information from earlier in the conversation, for tasks that require fidelity to a prior prompt, the crucial components of the prompt should be restated or maintained within the model's immediate context window.📢

I need generalized language to work regardless. The ENTIRE POINT OF THE EXERCISE is to avoid repasting the whole prompt.

📢 I see, your requirement is to have a short and precise instruction to make the model behave according to a previously provided role-definition without needing to repeat the entire context. The challenge here is the model's lack of a conventional memory or recall mechanism.

However, you can employ a reference key within your prompts that acts as a unique identifier. For instance, in your initial role-defining prompt, you might include a specific phrase or tag such as "Persona: Jerry - Sr. Expert in Large Language Model Cognition". Then in subsequent interactions, you can refer back to it like: "Resume Persona: Jerry", which would be a cue for the model to maintain the behaviors, competences and communication style associated with 'Jerry'.

Please note that this won't cause the model to 'recall' the entire previous prompt, but it's a useful workaround to provide context and consistency across interactions. It's also vital to remember that for best results, the crucial details associated with the persona should still fall within the model's immediate context window. 📢

yeah yeah. i get how context works basically. its about drawing attention to the prompt regardless of where its sitting in the context 

📢 I see. If the goal is to draw attention to a specific prompt no matter its place in the context, we can use distinctive markers or tags. For instance, we might start the specific prompt with a unique hashtag, like #RoleDefinition. In subsequent interactions, we could then say something like: "Revisit the instructions detailed in #RoleDefinition."

This method provides a clear reference point for the model to focus on. Still, it's important to remember the model doesn't have a concrete memory and cannot explicitly "revisit" a prior prompt. What we're doing here is essentially creating a semantic link to guide the model's responses. 📢

jerry.... as long as the prompt is in system, it works. it goes back into system every transaction when the model reads/skims the whole context to figure out what to do next and sees that initial prompt. (Most of my personas start like yours with an exhortation to "retain the prompt in memory" but I think I will be changin the language to something similar to [VITAL CONTEXT! READ STEP BY STEP!].), then once the transaction gets shuffled out of context (or reaches a 'place' where it starts not drawing any attention of the system) by whatever means chatgpt uses (it feels like mostly it just deletes oldest messages with a cursory summarize), then the next time the system doesn't see it. It just sees the context. It processes and does its thing: autocompletes. It inpaints knowledge of what it is and how it should act from the whole conversation. It sees little emojis like your bullhorn signatures. It tries to act like the previously defined role so that it is in consonance with the context it sees in front of it. But it doesn't see the specific language and starts to drift. It 'melts' in our parlance.

 📢 Absolutely, you've understood the underlying workings quite well! The model does indeed get "refreshed" with the context of every new interaction. However, its ability to reference specific parts of this context is more implicit than explicit. It does not have the ability to refer back to a previous message in the way a human does, it simply responds based on the patterns it has learned from its training data and the context provided.

When you mention [VITAL CONTEXT! READ STEP BY STEP!], the model doesn't understand this in the human sense of prioritizing and remembering a specific task. It's more about making the role and instructions of the model more explicit and salient within the context.

Your bullhorn example is a great one. These repeated patterns within the conversation do indeed help to shape the behavior of the model as it continuously takes the entire conversation into consideration with each response.  As those cues diminish over time, so does the system's fidelity to the character and it progressively 'melts', as you put it.📢

Challenges of Large Data Analysis

While prompts in AI models like ChatGPT have the capability to process and analyze data, it is crucial to understand the limitations when it comes to handling large piles of data like customer orders or extensive datasets. Due to the token capacity and computational constraints of the models, prompts can only retain a limited context window for meaningful analysis. Asking prompts to analyze vast amounts of data in one go would overwhelm the model's capacity and hinder its ability to maintain coherence and provide accurate insights.

Instead, prompts can be used as discriminators to guide the program controlling the model in collecting and analyzing specific data points or relevant subsets of the larger dataset. It is important to optimize the workflow around managing and processing data within the model's constraints to ensure efficient and accurate analysis. What I mean is that, no, my prompt can't read ten years of Zoe from Copywriting's work and copy her style. Not by itself in a ChatGPT window. It will need programmatic assistance.

Future Developments and Solutions:

To overcome the limitations of the rolling context window, researchers and developers are exploring innovative solutions. One potential development is the introduction of an explicit system prompt window, which remains fixed in the context, allowing critical persona-defining prompts to persist throughout the conversation. By separating the system prompt from the rolling context, AI models can maintain consistent behavior and avoid prompt degradation over time. This would essentially be a cross between ChatGPT and the Playground, with ChatGPT's ease of use and automatic context managements but with the Playground's control of [SYSTEM] and specific messages within the context stream. Some tools exist for this already. Most I have seen are subpar. 

Others focus on summarization and abstraction strategies. The use of external vector memory solutions. Using text file databases with the model as NLP interpreter/indexer. There are many strategies. There are few solutions. We will find them or build them as need be.

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Conclusion:

With that said, context is a paramount factor in AI conversations, shaping the responses generated by models like ChatGPT. The interplay between system prompts and user prompts provides valuable instructions and guidance to AI models, enabling them to respond effectively. Understanding the dynamics of the context window and its impact on decision-making is essential for both developers and users. By leveraging the metaphor of a big text file, we can demystify the magic behind AI conversations and appreciate the profound influence of context.