Generate tailored interview questions with GPT-4 based on CV, JD, and round

🤖 Smart Interview Assistant: Tailored Questions Based on CV, JD, and Round Watch the demo video below: !Watch the video 📌 Who’s it for This workflow is designed for: - Recruiters and Talent Acquisition Specialists who want to automate candidate interview prep. - Hiring Managers conducting multiple interviews and needing personalized question sets. - Technical Interviewers who want to save time and be well-prepared with relevant questions. ⚙️ How it works / What it does The Smart Interview Assistant automates the interview preparation process in a few clicks: 1. Accepts: - Multiple resumes (PDFs) - Selected job role - Chosen interview round 2. Extracts structured data from: - The candidate’s CV - The corresponding Job Description (JD) 3. Uses GPT-4 to analyze: - Candidate profile - Role requirements - Interview round context 4. Generates: - Tailored interview questions - Expected answers - A summarized interview prep report 5. Sends the report directly to the hiring team via email (SMTP) 📁 Google Drive Structure 📂 Root Folder ├── 📁 jd/ Stores all job descriptions in PDF format │ ├── BackendEngineer.pdf │ ├── AzureDevOpsLead.pdf │ └── ... └── 📄 Positions (Google Sheet) Maps Job Role ↔ JD File Link 📝 Sample Mapping Sheet: Positions Sheet Columns: - Job Role - Job Description File URL (pointing to PDF in jd/ folder) 🛠️ How to Set Up Step 1: Configure API Integrations - ✅ Connect your OpenAI GPT-4 API Key - ✅ Enable Google Cloud APIs: - Google Sheets API (to read job roles) - Google Drive API (to access CV and JD files) - ✅ Set up SMTP credentials (for email delivery) Step 2: Prepare Google Drive & Mapping Sheet 1. Create a root folder on Google Drive 2. Inside the root folder: - Create a folder named /jd/ and upload all job descriptions (PDFs) 3. Create a Google Sheet named Positions with the following format: | Job Role | Job Description File URL | |-----------------------------|--------------------------------------------| | Azure DevOps Engineer | https://drive.google.com/xxx/jd1.pdf | | Full-Stack Developer (.NET) | https://drive.google.com/xxx/jd2.pdf | Step 3: Build the Application Form Use any form tool (e.g., Typeform, Tally, or custom HTML) that collects: - 📎 Resume file (PDF) - 🧾 Job Role (dropdown) - 🔄 Interview Round (dropdown) Step 4: Resume & JD Extraction - 🔍 Use Extract from PDF to parse the resume content - 📄 Retrieve the JD link from the Positions sheet based on the selected Job Role - 🔗 Use Download file to pull the PDF for processing Step 5: Analyze with GPT-4 - Run both Resume and JD through a Profile Analyzer Agent (GPT-4 with JSON output) - Merge results - Add manual input or mapping for the Interview Round metadata Step 6: Generate Interview Report - Use a second GPT-4 agent (e.g., HR Expert Agent) to: - Generate 6–8 tailored interview questions - Include expected answers and rationale Step 7: Deliver Final Report - Format the content as: - 📄 PDF (optional) - 📨 Email body - Send the report to the recruiter, hiring manager, or interviewer via SMTP ✅ Requirements - 🔑 OpenAI GPT-4 API Key - 📁 Google Drive (for resume and JD storage) - 📊 Google Sheet (job role mapping) - 📬 SMTP credentials (host, username, password) - 🧰 n8n self-hosted or cloud instance with: - PDF Parser - Google Sheets node - HTTP Download node - Email node ✏️ How to Customize the Workflow | Part | Customization Options | |----------------------------|-------------------------------------------------------------| | Form UI | Modify the design, dropdown options, or input validations | | Job Description Source | Replace Google Sheet with Notion, Airtable, or database | | Interview Metadata | Add job level, region, or language preference | | AI Prompt Tuning | Adjust prompt phrasing or temperature in GPT nodes | | Report Format | Generate PDF instead of email body using PDF node | | Delivery Method | Add internal HR portal webhook or generate downloadable link |