Case Study: from divergent outputs → trustworthy selection → guided refinement
A product-style walkthrough that shows the workflow and the decision points—not a research summary.
Code assistants often force a binary accept/reject decision with limited transparency. Different models produce divergent solutions and verification shifts to humans.
- • Pick the best output quickly
- • Understand trade-offs beyond correctness
- • Improve weak dimensions without guessy prompting
Task: calculate gender distribution by country, create a bar chart, and save as distribution.png.
The comparison view summarizes each model solution into functional blocks, making divergence visible without line-by-line scanning.
Users inspect diagnostics to understand why a solution is risky: execution signals, efficiency indicators, and maintainability cues.
Guided refinement helps translate feedback into concrete edits. Users review diffs and keep control over accepting or reverting changes.
CGM Comparator turns verification into a repeatable product loop: compare structure → diagnose trade-offs → refine with guidance → track outcomes over time.
- • Faster comprehension
- • Higher confidence
- • Lower iteration overhead