Have you ever been working with artificial intelligence (AI), creating content, or performing any task, and everything is going great, only to suddenly start making mistakes or giving you inconsistent results? Have you considered that it might have become fatigued? Hard to believe? Think again. We’ll explain what AI fatigue is and how to avoid it.
Every morning, we use ChatGPT’s Projects feature to prepare the daily social media schedule for a client who publishes news on a news site. The process we follow consists of providing the client with a list of article URLs so that they can generate three post alternatives for each one. We ask them to read the content and, based on it, generate posts with emojis and hashtags.
However, we began to notice a pattern: while with the first articles it followed the entire process (reading + generating), with the last articles in the list it generated posts based on the titles, without reading the entire content. That’s an example of AI fatigue!
What is AI fatigue?
AI fatigue is a term that describes the decline in effectiveness of results generated by AI models as they are used repeatedly.
This common phenomenon occurs due to the technical and design limitations of AI models. Here are the reasons why this happens and how you can address it:
1. Context length limitations
- AI models have a limit on the amount of text they can process in a single interaction (called “context length”). If your prompt is too long, the model may “lose” some information toward the end.
- Solution: Break your prompt into smaller parts or even ask the AI to simplify it before giving it as an instruction.
2. Resource optimization
- Some AI systems are designed to optimize resource usage (processing time, energy, etc.). If the model detects that the prompt is too long, it can take shortcuts, such as simplifying tasks to save time.
- Solution: Make sure the prompt is clear and specific about the importance of reading the entire content. You can also add a warning like, “It is crucial that you pay special attention to these specific tasks.”
3. Bias towards initial information
- AI models tend to give more weight to information that appears at the beginning of the prompt. This is due to the design of their attentional mechanisms. Therefore, early instructions are processed correctly, but later ones may be ignored or treated superficially.
- Solution: Reorganize the information in your application so that everything has an equal chance of being processed correctly.
4. Lack of real-time feedback
- The model has no way of “realizing” that it’s omitting parts of the prompt unless you explicitly tell it to. Without feedback, it will continue to repeat the same behavior.
- Solution: If you notice that the model is not executing the complete commands, stop the execution and rephrase the prompt.
5. Problems with content extraction
- If your prompt is a workflow that automatically extracts content from websites, there may be technical issues when accessing articles (e.g., website blocks, request rate limits, etc.). This could cause the model to look for simpler ways to accomplish the task.
- Solution: Verify that the system is correctly extracting content from all URLs. If necessary, manually copy and paste the text.
How to improve your process:
- Divide and conquer: Send items in smaller batches (e.g., 2-3 at a time). Use the Chain of Thought technique, in which the AI is guided to “think step by step” before giving a final answer. This is especially useful for complex tasks that require logical reasoning or problem-solving. Instead of giving a direct answer, the AI breaks down its thought process into intermediate steps.
- Reinforce the prompt: Use clear and emphatic language, such as: “Please, before generating your response, review the document I provided twice. Don’t skip any steps. If you have any questions or problems, it’s best to stop the task and let me know.”
- Review the output: If you notice that the model is not following the instructions, stop the process and adjust the prompt.
- Try different models or tools: Some AI systems can handle long and complex tasks better than others.
In short, AI fatigue occurs due to technical limitations in processing long, repetitive instructions and optimizing resources. By breaking down the task and being more specific in your instructions, you can improve the accuracy and consistency of your results.
