Source URL: https://www.tomtunguz.com/user-perception-quality/
Source: Tomasz Tunguz
Title: My Prompt, My Reality
Feedly Summary:
“Now with LLMs, a bunch of the perceived quality depends on your prompt. So you have users that are prompting with different skills or different level of skills. And the outcome of that prompt may be perceived as low quality, but that’s something that is really hard to control.”
Loïc Houssier, VP Product at Superhuman, shared this perspective on a recent podcast.
AI products differ from classic software in that the experience is in large part determined by the user.
Software has always had a learning curve; master Photoshop, for example, and you can apply Bezier curves consistently, just like any other skilled user.
AI products selling outcomes, operate differently. The ideal output isn’t an identical fixed output achievable by all skilled users.
Instead, it’s a collaboration where expert prompts can lead to a spectrum of valid results based on nuanced intent and context.
How can product teams manage this? They can rewrite the user prompt – many are – to expand on the user intent and steer a basic query into a more nuanced & ultimately successful answer.
Even then, anticipating how a user might want to steer the AI is hard.
One product technique I’ve found very useful is a series of follow up questions. ChatGPT does this very well – like in this example above, asking for refinement on a broad query.
Just like a colleague asking for clarity, the AI seeks guidance. More than just asking for greater insight, the questions help me understand my request better.
AI Summary and Description: Yes
Summary: The text discusses the challenges associated with using large language models (LLMs) in AI products, emphasizing that the quality of AI output largely relies on user input. It highlights the distinction between traditional software and AI in terms of interaction and results, suggesting techniques for enhancing user prompts.
Detailed Description: The commentary by Loïc Houssier provides valuable insights into the increasing complexity of user interaction with AI-driven products. The following points underscore the significance of the content in the context of AI and user experience:
– **User Interaction with AI**: Unlike traditional software, where outputs are more predictable based on user proficiency, AI products like LLMs are more dependent on the quality of user prompts.
– **Quality of Output**: The perceived quality of AI outcomes varies significantly based on user skill; less experienced users may produce lower-quality prompts, resulting in suboptimal responses.
– **Managing User Intent**: Product teams face the challenge of guiding users to construct better prompts, which can be complex due to varying user capabilities and intent.
– **Techniques to Enhance Prompts**:
– **Rewriting Prompts**: Product teams are encouraged to revise user prompts to clarify intent and assist users in generating more effective queries.
– **Follow-Up Questions**: Implementing a system of follow-up questions can improve user understanding and engagement, enabling AI to provide refined and more accurate responses.
– **AI’s Role in Clarification**: The example of ChatGPT illustrates how LLMs can emulate a collaborative dialogue by asking for additional information, paralleling real human interactions.
Overall, the text highlights notable trends in AI development where the emphasis is shifting from mere software usability to an adaptable, interactive user experience model, crucial for professionals involved in AI security and product management. Understanding these dynamics is essential for developing more user-friendly AI systems and addressing potential security concerns stemming from varied user interactions and the effectiveness of LLMs.