The video explains how ChatGPT handles conflicting instructions by interpreting and prioritizing one instruction over another based on context, without recognizing the contradiction. It emphasizes the importance of clear and unambiguous prompts, as vague instructions can lead to unsatisfactory or misaligned responses.
The video discusses how ChatGPT handles conflicting instructions given by users. When users provide contradictory prompts, such as asking for a response that is both casual and formal, ChatGPT does not explicitly recognize the conflict. Instead, it attempts to interpret the instructions and predict the most appropriate response based on patterns it has learned from previous data.
ChatGPT’s approach to conflicting instructions relies heavily on the context and the strength of the instructions provided. If one instruction appears more dominant or familiar, the model is likely to prioritize that instruction over others. This means that the model does not engage in reasoning to resolve contradictions; rather, it simply selects a direction based on its training and the input it receives.
The video emphasizes the importance of prompt clarity when interacting with ChatGPT. Vague or mixed signals in prompts can lead to ambiguous or unsatisfactory responses. If users are not clear in their instructions, the model may produce outputs that do not align with the user’s expectations, as it tries to fill in the gaps based on its training data.
Additionally, the video highlights that ChatGPT does not stop to question the coherence of the instructions it receives. Instead, it generates responses that it believes are the best fit, even if they may not fully address the conflicting nature of the prompt. This can result in answers that sound intelligent but may miss the mark in terms of the user’s intent.
In conclusion, the video illustrates that AI models like ChatGPT do not resolve conflicts in instructions but rather adapt to them by choosing one instruction to follow. This behavior underscores the necessity for users to provide clear and unambiguous prompts to achieve the desired outcomes in their interactions with the model.