GPT-5 Memory Issues: The Context Window Problem Explained

8/12/2025

GPT-5 Context Persistence Problems: Why It Can't Remember Your Previous Messages

If you’ve been playing around with the new GPT-5, you might’ve noticed something… a little frustrating. You get into a great flow, you’re a dozen messages deep into a conversation, & then BAM. It completely forgets a key detail from three messages ago. It’s like talking to someone with short-term memory loss, & honestly, it can be a real workflow killer.

You're not going crazy. This is a real, well-documented issue. In fact, Reddit threads are filled with users complaining that the "upgrade" to GPT-5 feels more like a downgrade in the memory department. People have reported hitting their usage limits much faster & having sessions that once worked perfectly now devolve into a mess of repeated questions & forgotten context.

So, what's REALLY going on here? Why does a model that feels like it's on the brink of AGI still struggle with something as basic as remembering what you just told it? Turns out, the answer is pretty complex, & it goes to the very core of how these large language models (LLMs) are built. Let's dive in.

The Elephant in the Room: The "Context Window"

First things first, we need to talk about the "context window." Think of it as the AI's short-term memory or its working RAM. It’s the amount of information the model can "see" at any given moment when it's talking to you. This includes your message, the previous messages in the conversation, & any files or documents you've uploaded.

Everything inside this window is what the model uses to generate its next response. Anything outside of it is, for all intents & purposes, forgotten. It doesn't get stored in some long-term memory bank. It’s just… gone.

With the launch of GPT-5, OpenAI set some pretty clear limits on these context windows, depending on your subscription level.

Free users: Get an 8,000-token window.
Plus subscribers: Get a 32,000-token window.
Pro & Enterprise users: Get a 128,000-token window.

To put that into perspective, a token is roughly equivalent to about 4 characters of text. So, an 8,000-token window is only about 6,000 words. If you're having a detailed conversation or trying to analyze a couple of articles, you can max that out pretty quickly. Some users who were accustomed to the ~64k token context of previous models felt this was a significant step back, especially for paying customers.

While the API version of GPT-5 boasts a much larger 400,000-token context window, that’s not what most people experience in their day-to-day use of ChatGPT. This discrepancy between the API & the public-facing product is likely due to the immense cost & latency of running millions of chats with such a large context.

The "Why": It's Not a Bug, It's a Feature (of the Architecture)

This is where things get a bit technical, but stick with me. The root of the problem lies in the groundbreaking architecture that makes these models possible in the first place: the Transformer.

The Transformer model, introduced in a 2017 paper titled "Attention Is All You Need," was a revolution because it got rid of the sequential processing of older models like RNNs & LSTMs. Instead of reading text word-by-word, it could look at the entire sequence at once using a mechanism called self-attention.

Self-attention is what allows the model to weigh the importance of different words in the input text & understand the relationships between them, even if they're far apart. This is what gives models like GPT their incredible ability to understand context & nuance.

But here’s the catch: the self-attention mechanism has a dark side. Its computational complexity grows quadratically with the length of the input sequence. In simple terms, if you double the length of the text you feed it, the computational power required to process it quadruples. If you triple the length, it goes up by a factor of nine. You can see how this gets out of hand VERY quickly.

This quadratic scaling leads to a few major limitations:

Massive Computational Cost: Processing very long sequences requires an enormous amount of computational power, which is both expensive & energy-intensive.
Large Memory Requirements: The model needs to store a ton of intermediate calculations (called activations) in memory, which also limits the length of the sequence it can handle.
Fixed Sequence Length: The architecture is built around a fixed-size input, making it inherently difficult to handle ever-growing conversations.

So, the very thing that makes GPT so powerful is also its Achilles' heel. It's a fundamental architectural trade-off. The model doesn't have a true, continuous "memory" in the human sense. It just has a very large, but ultimately limited, window of text that it can look at.

The Workarounds: How We're "Hacking" Memory into LLMs

So, are we just stuck with forgetful AI forever? Not exactly. A ton of brilliant people are working on this problem, & there are some pretty clever ways to get around these limitations. The most popular & effective one right now is called Retrieval-Augmented Generation, or RAG.

Retrieval-Augmented Generation (RAG): The "Open-Book Exam" for AI

If the context window is the AI's short-term memory, think of RAG as giving the AI an "open-book exam." Instead of trying to cram all the information into the context window (the book), RAG allows the model to look up relevant information from an external knowledge base before it generates a response.

Here’s how it works in a nutshell:

When you ask a question, the system first uses a retrieval mechanism (like a search algorithm) to find the most relevant chunks of information from a pre-approved knowledge source (like a company's internal documents, a product manual, or a specific dataset).
It then takes this retrieved information & "augments" the original prompt with it, feeding both to the LLM.
The LLM then generates an answer based on both your original question & the fresh, relevant context it was just given.

This approach is a game-changer for a few reasons:

It provides up-to-date information: LLMs are only as smart as the data they were trained on, which has a cutoff date. RAG allows them to access & use information that's current.
It reduces hallucinations: By grounding the model's response in a specific set of facts, it's less likely to make things up when it doesn't know the answer.
It enables domain-specific knowledge: This is HUGE for businesses. You can create a knowledge base with your company's proprietary data, product information, & customer support documents.

This is where a tool like Arsturn becomes incredibly powerful. Arsturn helps businesses build no-code AI chatbots that are trained on their own data. Under the hood, it's leveraging principles similar to RAG. You can feed it your website content, help docs, & product catalogs, & it creates a custom AI that can answer customer questions with information that's actually accurate & specific to your business. It’s a perfect example of how to solve the context problem for practical applications. Instead of a generic chatbot that gives generic answers, you get an expert on your business, available 24/7 to engage with visitors & provide instant support.

Beyond RAG: The Future of LLM Memory

RAG is the dominant solution right now, but it's not the only one. Researchers are exploring a bunch of other exciting techniques to give LLMs more robust & persistent memory. These can be thought of as different layers of memory.

Sequential Chaining & Compression: This is the simplest method, just appending new messages to the history. To manage length, developers use techniques like summarizing older parts of the conversation or creating rolling summaries to compress the information.
Hierarchical Memory: This involves creating different levels of memory, similar to a computer. There's a short-term, high-detail memory (the context window), & then a long-term memory that stores more abstract summaries or key points from past conversations.
Persistent Long-Term Memory: This is the holy grail. The idea is to create a "diary" for the LLM, where it deliberately stores key facts, user preferences, & important details from conversations in a structured database. This would allow a personal AI to remember your kid's birthday or a coding assistant to remember the architecture of your project across multiple sessions. Some projects, like LLM4LLM, are already showing incredible results by connecting LLMs to SQL databases, achieving over 90% accuracy in recall tasks where baseline models fail completely.

So, What's the Bottom Line?

The reason GPT-5 can feel so forgetful isn't because it's broken or because OpenAI isn't trying. It's because of a fundamental architectural limitation in the Transformer models that power it. The quadratic scaling of the attention mechanism makes having an infinitely long "memory" computationally impossible right now.

However, the field is moving at lightning speed. Techniques like Retrieval-Augmented Generation (RAG) are already providing a powerful workaround, especially for businesses. By connecting LLMs to external knowledge bases, companies can create highly effective & knowledgeable AI assistants.

For businesses looking to leverage this, the path forward is becoming clearer. It's not about waiting for a hypothetical GPT-6 with infinite memory. It's about using the smart tools available today. This is exactly the problem Arsturn is built to solve. It allows businesses to take their own curated data & build a custom AI chatbot on top of it. This creates a conversational AI that doesn't need to remember everything about the world, it just needs to be an expert in one thing: your business. It can generate leads, boost conversions, & provide personalized customer experiences because its "memory" is your own, dedicated knowledge base.

It’s a pretty exciting time. While we're still a ways off from AI that remembers every detail of every conversation, the solutions being developed are making these tools more practical, reliable, & powerful every single day.

Hope this was helpful & shed some light on what’s happening behind the curtain. Let me know what you think