An AI-powered, end-to-end interview preparation and mentoring automation system for campus placements. It enables placement cells to generate hyper-personalized 4-page interview preparation PDFs for shortlisted students, by combining job descriptions (JDs), candidate data, and LLMs via LangChain and Ollama.
Note: This template requires self-hosted n8n to run community nodes like LangChain and Ollama.
Accepts a CSV of shortlisted students and a JD via form upload
Analyzes student profile vs JD using Ollama LLM via LangChain
Generates personalized interview preparation PDFs
Sends the PDF to each student via email
Logs all data in Google Sheets and prevents duplicate processing
📷 Please add a workflow screenshot here showing the main nodes and flow
CSV of shortlisted students + JD + company name is submitted via HTTP Request form trigger.
CSV parsed → structured rows added to Google Sheet named with company + batch.
Only students with N8N_Agent = Not Generated are selected to avoid reprocessing.
LangChain agent (via Ollama + Gemini Search Tool) generates a 4-page Markdown report:
Page 1: Profile Summary, Skill Gap Analysis, Company Insights
Page 2: 15–20 Personalized Interview Questions
Page 3: 5 Group Discussion Topics + Strategy
Page 4: Custom Preparation Plan + Suggested Resources
Markdown → Stylish PDF via APITemplate.io
Each student receives a personalized email with the attached report.
Marks the student’s row as “Generated” in N8N_Agent column.
Self-hosted n8n with Community Nodes enabled
Local or Docker-hosted Ollama with LLaMA3.2 or equivalent model
Activated LangChain and Gemini Search Tool nodes
APITemplate.io API Key
Connected Google Sheets account
SMTP setup or Gmail node for email delivery
Replace the LLM prompt in the LangChain node with your own tone/style
Modify the PDF template on APITemplate.io to reflect your institution branding
Update the email copy for formal or informal tones
Add new filters (e.g., minimum CGPA, branch) for student selection