Since a few of you asked about the AI generation quality in the comments, I wanted to share a quick behind-the-scenes look at how the models behind VitoCV actually work.
When I started building this, I realized standard LLMs are often too generic or 'fluffy' for resumes. To fix this, I focused heavily on prompt engineering and fine-tuning the AI specifically against real-world ATS constraints and FAANG resume guidelines.
Specifically, the AI is trained to:
Prioritize Action Verbs: It automatically restructures sentences to start with strong, impactful action verbs.
Quantify Results: It actively looks for metrics in your input (or prompts you for them) to format points in the classic 'Accomplished [X] as measured by [Y], by doing [Z]' format.
ATS Keyword Mapping: It cross-references your bullet points with standard industry keywords so the ATS parsers don't filter you out.
Build your FAANG-ready professional resume in 5 minutes. VitoCV uses advanced AI to craft perfect bullet points, check ATS scores, and prepare you for interviews.
