Description This workflow automates the evaluation of interviewer feedback using AI. It retrieves raw notes from Google Sheets, processes them through GPT-4o-mini for structured scoring, validates outputs, and calculates weighted quality scores. The system provides real-time Slack feedback to interviewers, logs AI errors for transparency, and recommends training if the feedback quality is low. What This Template Does (Step-by-Step) ⚡ Manual Trigger – Runs the workflow manually to start evaluation. 📋 Fetch Raw Feedback Data (Google Sheets) – Reads all feedback entries (Role, Stage, Interviewer Email, Feedback Text, row_number). 🧠 AI Quality Evaluator (Azure GPT-4o-mini) – Processes feedback into structured JSON across 5 dimensions. 🔍 Analyze Feedback Quality (LLM Chain) – Applies scoring rules (Specificity, STAR, Bias-Free, Actionability, Depth) and outputs structured JSON. ✅ Validate AI Response – Ensures AI output isn’t undefined or malformed. 🚨 Log AI Errors (Google Sheets) – Records invalid AI responses for debugging and auditing. 🔄 Parse AI JSON Output (Code Node) – Converts AI JSON text into structured n8n objects with error handling. 🧮 Calculate Weighted Quality Score (Code Node) – Computes final weighted score (0–100), generates flags, formats vague phrases, and preserves context. 💾 Save Scores to Spreadsheet (Google Sheets) – Updates the original feedback row with Score, Flags, and AI JSON. 💬 Send Feedback Summary to Interviewer (Slack) – Sends interviewers a structured Slack report (score, flags, vague phrases, STAR improvement tips). 🎯 Check if Training Needed – Applies threshold logic: if score < 50, route to training recommendations. 📚 Send Training Recommendations (Slack) – Delivers STAR method guides and bias-free interviewing resources to low scorers. Prerequisites Google Sheets (Raw_Feedback + Error Log Sheet) Azure OpenAI API credentials (for GPT-4o-mini) Slack API credentials (for sending feedback & training notifications) n8n instance (cloud or self-hosted) Key Benefits ✅ Automated interview feedback quality scoring ✅ Bias detection and vague feedback flagging ✅ Real-time Slack feedback to interviewers ✅ Error logging for AI reliability tracking ✅ Training recommendations for low scorers ✅ Audit trail maintained in Google Sheets Perfect For HR & Recruitment teams ensuring structured interviewer feedback Organizations enforcing STAR method & bias-free hiring Teams seeking continuous interviewer coaching Companies needing audit-ready records of interview quality