From a user’s perspective, Chat GPT appears very smart and efficient. This is because you give it a detailed and complex prompt. Therefore, it replies with a pretty complex response. Those responses look intelligent and sophisticated on the surface. Because of this, it’s easy to assume the system fully understands the task and is capable of handling any level of complexity, even though the apparent complexity is often driven by language fluency rather than deep reasoning, understanding, or reliability in complex situations.
Well, the fact is that I have spent the past almost two years now working with chat GPT daily, and while its capabilities are incredible, there are many, many times where it breaks down and simply cannot do things as well as a human. So What are those situations where it can’t perform as well? Well, let’s get into some of the chat GPT limitations.
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The Key Chat GPT Limitations
Chat GPT seems very helpful but as soon as tasks become complex, it becomes difficult for it to keep up with the user’s expectations. Some of the main limitations are:
- Context Window Limitation
- Context Loss Over Time
- Prompt Complexity Breakdown
- Function Calling Reliability Issues
- Limited Understanding of Complex Concepts
1. Context Window Limitation
One of the key ChatGPT limitations is the context window. It represents the memory size of ChatGPT, meaning how much data it can hold in its own memory so that it maintains a full context of all the information you have sent within that window.
In the most recent model, the context window is 128,000 tokens. That sounds like a massive amount, and it is a large amount of text that the model can handle, but the question is how you can know for sure the size of that limit.
This is where a GPT-4 token counter becomes useful. The reason this tool is preferred over the OpenAI one is that it tells you exactly how many tokens you are using, rather than simply stating that you are over the limit. What this shows is how easy it is to exceed the token limit, which becomes a real limitation, especially for more complex tasks such as coding with ChatGPT.
For example, if I wanted ChatGPT to make changes to the Stammer landing page in this way, I could not even send the entire source code for the landing page. Even though it is a very small landing page, it is still easy to exceed the context window.
2. Context Loss Over Time
The next chat GPT limitation is context loss over time. One thing to keep in mind with the context window is that it is cumulative for all the messages you send within a chat GPT conversation. Every time you send a new message, it sends the entire chat history so it can have that full context, including its own previous responses.
In the chat GPT interface, there is JavaScript code running every time in the background that counts tokens, and if the tokens exceed the model limit, it drops some of the prior message history. That’s why, as you keep going, it sometimes starts to forget older messages.
Keep in mind it does have a limited memory. It can’t keep a context going for every single piece of information you send it, and as the conversation gets too large, some messages may start dropping.
3. Prompt Complexity Breakdown
The next chat GPT limitation is prompt complexity breakdown. As the complexity of the prompt increases, the AI can have a tendency to ignore instructions. It starts to do strange things and undefined behavior starts to happen.
As your prompt gets more complex, chat GPT starts to ignore instructions and sometimes not do things at all. Reliability can be an issue in general as the prompt gets too complex, and that is a current limitation.
4. Function Calling Reliability Issues
Function calling reliability is the chat GPT limitation that means chat gpt wont follow instructions properly especially when it has to perform multi-step actions. Chat GPT 3.5 can’t even handle scheduling and lead generation properly. Even when we clearly explain how a function works and how to interact with our code, it simply starts to ignore those instructions even when it’s well within the context window.
Specifically, the main problem with function calling is that when prompts become too complex sometimes it will suddenly forget to call the function. At a certain point, instead of triggering the required action in the code, it simply skips that step. This becomes a reliability issue when prompts get too complex.
5. Limited Understanding of Complex Concepts
One of the main chat GPT limitations is that it stumbles while doing complex and multi-step tasks that seem simple on the surface level but involves multiple steps like judgement and contextual thinking. For example, choosing and scheduling an appointment.
Suppose when you ask any human agent to schedule an appointment, they will automatically get that they have to check the availability, ask the customer about suitable time, adjust if it doesn’t, pick another time, and then confirm the appointment.
In case of chat GPT, you cannot simply ask it to schedule a meeting and then expect it to figure out all the steps on its own. You have to give proper instructions and detailed prompts about each and every step involved in the whole process.
Humans intuitively understand context, intent, and next steps, while AI systems need every part of the task to be clearly spelled out. Without extremely detailed instructions, ChatGPT can miss steps, make mistakes, or fail to complete the task properly.
FAQs about Chat GPT Limitation
1. Why does ChatGPT seem smart but struggle with real-world tasks?
Chat GPT seems very helpful but as soon as tasks become complex, it becomes difficult for it to keep up with the user’s expectations due to following limitations
- Context Window Limitation
- Context Loss Over Time
- Prompt Complexity Breakdown
- Function Calling Reliability Issues
- Limited Understanding of Complex Concepts
2. What is the context window limitation in ChatGPT?
The context window represents the memory size of ChatGPT. It means how much data it can hold in its own memory so that it maintains a full context of all the information you have sent within that window. In the most recent model, the context window is 128,000 tokens.
3. Why does ChatGPT forget earlier instructions in long conversations?
ChatGPT forgot earlier instructions in long conversations due to context window limitation and context loss over time. In the chat GPT interface, there is JavaScript code running every time in the background that counts tokens, and if the tokens exceed the model limit, it drops some of the prior message history. That’s why, as you keep going, it sometimes starts to forget older messages.
4. Why does ChatGPT ignore instructions when prompts become complex?
ChatGPT ignores instructions when prompts become complex because it has limited memory and it loses the context as tokens exceed the model limit.