---
title: "Trust Calibration"
description: "1 posts about Trust Calibration, summarised, each linking to the original."
---

# Trust Calibration
> 1 posts about Trust Calibration, summarised, each linking to the original.

## Articles

### [Our Framework for Reviewing AI-Generated Code](https://yomu.fyi/post/our-framework-for-reviewing-ai-generated-code.md)
- Company: [Jetbrains](https://yomu.fyi/company/jetbrains.md)
- Author: Katie Fraser
- Published: Oct 6, 2026
- Type: Explainer

JetBrains Research, in collaboration with Lund University, studied how to review AI-generated code as AI agents produce thousands of lines across multiple files quickly. The core problem identified is trust calibration: developers cannot gauge an LLM’s confidence per line, making line-by-line review unscalable. The study proposes a three-level conceptual framework: overview for high-level hypotheses, file-level risk stratification, and code snippet-level chunk decomposition with chain-of-thought linkage. This framework was developed through participatory design with 17 practitioners and a follow-up survey of 43 professionals, and will be presented at ESEIW 2026. The paper argues that current diff viewers are inadequate for AI-generated code and that tools must surface risk and confidence signals at the granularity where developers allocate attention.