---
title: "SaaS Competitive Advantage Through Elegant LLM Feedback Mechanisms"
description: "Discover how leading SaaS companies leverage LLM feedback loops to build trust and competitive advantage. Key insights for startup founders and tech leaders."
categories: ["product","AI"]
keywords: ["SaaS","LLM feedback mechanisms","Google Bard","product feedback","competitive advantage","Tomasz Tunguz","AI trust","software development","user experience","feedback loops"]
ai_summary: "Explore how SaaS companies use LLM feedback loops to enhance user trust and gain a competitive edge."
date: 2023-10-03
lastmod: 2026-07-20
canonical_url: https://www.tomtunguz.com/easy-feedback-ml-bard/
author: "Tomasz Tunguz"
---

Eliciting product feedback elegantly is a competitive advantage for LLM-software.

![image](https://res.cloudinary.com/dzawgnnlr/image/upload/smao9huamftyh3abtoao.png)

Over the weekend, I queried Google's Bard, & noticed the elegant feedback loop the product team has incorporated into their product.

I asked Bard to compare the 3rd-row leg room of the leading 7-passenger SUVs. 

![image](https://res.cloudinary.com/dzawgnnlr/image/upload/jsmaz1feupmctopagvuq.png)

At the bottom of the post is a little G button, which double-checks the response using Google searches. 




[![image](https://res.cloudinary.com/dzawgnnlr/image/upload/ilzrwhpupayocaors5en.png)](https://res.cloudinary.com/dzawgnnlr/image/upload/ilzrwhpupayocaors5en.png)
I decided to click it. This is what I would be doing in any case ; spot-checking some of the results. 

LLM systems aren't deterministic. [1 can be larger than 4 for an LLM](https://tomtunguz.com/yes-or-no-chatgpt/). If an LLM produces a few spurious results, the user won't trust it.  
[![image](https://res.cloudinary.com/dzawgnnlr/image/upload/vza4mdor0wtneswxqb2r.png)](https://res.cloudinary.com/dzawgnnlr/image/upload/vza4mdor0wtneswxqb2r.png)
Bard highlights confirmed data in green & potentially erroneous data in red. I confirmed the green is correct. The red sometimes was correct and other times wasn't. 

In addition to saving me time, I can use a less-than-trusted system, benefit from the accurate portion of the response - which should keep me coming back - all while improving the system for the next time. 

It's symbiotic. 

I wonder if it won't become the dominant feedback mechanism for LLM-enabled apps, replacing the now ubiquitous but deeply amorphous thumbs up/down.