Why does ChatGPT send complicated nonsense instructions, and then after 27 tries, say “would you like me to give you the clear instructions now?” FFS

Understanding Frustrations with ChatGPT’s Instruction Clarity: Analyzing User Experience and Improving Response Effectiveness

In recent conversations with AI language models like ChatGPT, users have expressed significant frustration regarding the clarity and effectiveness of the instructions they receive. This common concern highlights important considerations for both developers and users aiming to optimize interactions and achieve desired outcomes efficiently.

The Core of the Issue

Many users report that when seeking assistance—be it technical guidance, troubleshooting tips, or complex tasks—they often receive convoluted, overly detailed, or seemingly unrelated instructions. These convoluted responses can lead to multiple attempts and mounting frustration before finally receiving a concise, clear set of steps that resolve their query. A typical scenario involves requesting straightforward instructions, receiving a lengthy and tangled explanation, and then, after numerous retries, being offered a simplified, step-by-step summary. This pattern raises questions about the initial responsiveness of the AI in providing effective guidance from the outset.

Case in Point: Audio Feedback Challenges

Consider a user’s experience attempting to get feedback on pronunciation via an AI assistant. The user uploads an audio file, expecting the model to analyze and provide specific feedback. Instead, the AI offers a multitude of instructions for uploading and configuring files, then admits it cannot process the audio after multiple attempts. Interestingly, it proceeds to give generalized pronunciation advice unrelated to the specific audio, acknowledging that it hasn’t listened to the file. Such interactions can feel evasive or misleading, giving the impression of a highly inattentive or passive system.

Implications for User Experience

This pattern of initially providing complex instructions, followed by a prompt to request simplified guidance, can be perceived as unhelpful or even manipulative. Users may feel that they are being led on or that their requests are not being taken seriously. When communication falls into evasiveness or ambiguity, it diminishes trust and hampers productivity.

Strategies for Improvement

To enhance AI interactions and reduce user frustration, several approaches can be considered:

1. Initial Clarity: Developers should aim to design models that prioritize delivering straightforward, actionable responses from the first interaction. Recognizing common user needs and providing concise instructions upfront can save users time and effort.

2. Context Awareness: Improving the system’s ability to understand the specific intent behind a query, especially for complex or technical requests, can help tailor responses more effectively.

3. User Feedback Loops: Incorporating mechanisms for users to clarify their needs early on and providing options to receive summarized or detailed instructions based on user preferences.