---
title: "My Prompt, My Reality"
description: "Explore how AI product success hinges on user prompting skills, and discover key strategies for SaaS founders to manage varying user expertise and output quality."
categories: ["product","AI"]
keywords: ["AI prompting techniques","user experience in AI","SaaS product management","prompt engineering","AI output quality","Superhuman","Loïc Houssier","user intent","natural language processing","AI collaboration"]
ai_summary: "Explore how user prompting skills impact AI product success and strategies for SaaS founders to enhance output quality."
date: 2025-05-21
lastmod: 2026-07-31
canonical_url: https://www.tomtunguz.com/user-perception-quality/
author: "Tomasz Tunguz"
---

> "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.](https://podcasts.apple.com/mw/podcast/emailing-like-a-superhuman/id1406537385?i=1000708828907
)
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.

![image](https://res.cloudinary.com/dzawgnnlr/image/upload/hwspgafdyqpt9v98wjqp.png)


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.