Hey r/ArtificialInteligence, I built a 3-agent AI pipeline for LinkedIn voice cloning — here’s what I learned about style transfer Built a system that analyzes someone’s LinkedIn writing style and generates new posts matching their voice. The interesting technical part: Architecture: - Agent 1 (GPT-4o): 3-step voice analysis — maps sentence structure, vocabulary patterns, rhetorical style - Agent 2 (GPT-4o-mini): Generates posts using the voice profile - Agent 3 (GPT-4o): Scores output 0-100 for authenticity, auto-rejects below threshold Key finding: Single-shot voice analysis gets ~60% accuracy. A 3-step pipeline where each step builds on the previous gets to 90%+. The difference is using separate models for analysis vs generation — reduces echo chamber effect. Unexpected challenge: The AI kept fabricating specific facts from the voice samples (dates, companies, achievements). Had to explicitly instruct “style reference only, never fabricate facts.” Curious if anyone’s worked on similar style-transfer problems. How do you handle the tradeoff between matching someone’s voice patterns and keeping the content factually grounded? Project: kraflio.com (launched on PH today) submitted by /u/Soft_Ad6760
Originally posted by u/Soft_Ad6760 on r/ArtificialInteligence