# Trust Calibration for AI Software Builders

DevFeed: [Trust Calibration for AI Software Builders](<https://devfeed.tech/articles/trust-calibration-for-ai-software-builders-1721.md>)

Original publisher: [Read original article](<https://fly.io/blog/trust-calibration-for-ai-software-builders/>)

Published: 2025-08-18T00:00:00Z

Content type: article

Language: en

Sources: [The Fly Blog](<https://devfeed.tech/sources/the-fly-blog.md>)

Topics: [Interaction Design](<https://devfeed.tech/topics/interaction-design.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [cursor](<https://devfeed.tech/topics/cursor.md>), [Software](<https://devfeed.tech/topics/software.md>), [Code](<https://devfeed.tech/topics/code.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [article](<https://devfeed.tech/tags/article.md>), [cdn](<https://devfeed.tech/tags/cdn.md>), [close-to-users](<https://devfeed.tech/tags/close-to-users.md>), [code](<https://devfeed.tech/tags/code.md>), [cursor](<https://devfeed.tech/tags/cursor.md>), [deploy-app-servers](<https://devfeed.tech/tags/deploy-app-servers.md>), [docker](<https://devfeed.tech/tags/docker.md>), [elixir](<https://devfeed.tech/tags/elixir.md>), [fly](<https://devfeed.tech/tags/fly.md>), [fly-io](<https://devfeed.tech/tags/fly-io.md>), [heroku-alternative](<https://devfeed.tech/tags/heroku-alternative.md>), [heroku-competitor](<https://devfeed.tech/tags/heroku-competitor.md>), [hosting](<https://devfeed.tech/tags/hosting.md>), [i](<https://devfeed.tech/tags/i.md>), [interaction-design](<https://devfeed.tech/tags/interaction-design.md>), [mental-models](<https://devfeed.tech/tags/mental-models.md>), [networking](<https://devfeed.tech/tags/networking.md>), [postgresql-clusters](<https://devfeed.tech/tags/postgresql-clusters.md>), [servers](<https://devfeed.tech/tags/servers.md>), [software](<https://devfeed.tech/tags/software.md>)

## AI overview

The article explains trust calibration for AI software products: aligning users' trust with a system's actual capabilities and limitations. It discusses the risks of over-trust and under-trust, advocates accurate user mental models, and uses Cursor's change highlighting as an example of communicating that model-generated code is a suggestion rather than a command.

## Source excerpt

Trust calibration is a concept from the world of human-machine interaction design, one that is super relevant to AI software builders. Trust calibration is the practice of aligning the level of trust that users have in our products with its actual capabilities. If we build things that our users trust too blindly, we risk facilitating dangerous or destructive interactions that can permanently turn users off. If they don't trust our product enough, it will feel useless or less capable than it actually is. So what does trust calibration look like in practice and how do we achieve it? A 2023 study reviewed over 1000 papers on trust and trust calibration in human / automated systems (properly referenced at the end of this article). It holds some pretty eye-opening insights - and some inconvenient truths - for people building AI software. I've tried to extract just the juicy bits below. Limiting Trust Let's begin with a critical point. There is a limit to how deeply we want users to trust our products. Designing for calibrated trust is the goal, not more trust at any cost. Shoddy trust calibration leads to two equally undesirable outcomes: Over-trust causes users to rely on AI systems in situations where they shouldn't (I told my code assistant to fix a bug in prod and went to bed). Under-trust causes users to reject AI assistance even when it would be beneficial, resulting in reduced perception of value and increased user workload. What does calibrated trust look like for your product? It's important to understand that determining this is less about trying to diagram a set of abstract trust parameters and more about helping users develop accurate mental models of your product's capabilities and limitations. In most cases, this requires thinking beyond the trust calibration mechanisms we default to, like confidence scores. For example, Cursor's most prominent trust calibration mechanism is its change suggestion highlighting. The code that the model suggests we change is h