{
  "workflow": {
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    "name": "Generate tailored interview questions with GPT-4 based on CV, JD, and round",
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    "createdAt": "2025-08-01T08:29:51.767Z",
    "description": "# 🤖 Smart Interview Assistant: Tailored Questions Based on CV, JD, and Round\n### Watch the demo video below:\n[![Watch the video](https://s3.ap-southeast-1.amazonaws.com/automatewith.me/smart-interview-prep-assistant-automate-candidate-reports-and-custom-questions-with-n8n-ai.jpg)](https://youtu.be/-KHze-bRpqQ)\n## 📌 Who’s it for\nThis workflow is designed for:\n- **Recruiters** and **Talent Acquisition Specialists** who want to automate candidate interview prep.\n- **Hiring Managers** conducting multiple interviews and needing personalized question sets.\n- **Technical Interviewers** who want to save time and be well-prepared with relevant questions.\n\n## ⚙️ How it works / What it does\nThe Smart Interview Assistant automates the interview preparation process in a few clicks:\n\n1. Accepts:\n   - **Multiple resumes (PDFs)**\n   - **Selected job role**\n   - **Chosen interview round**\n2. Extracts structured data from:\n   - The candidate’s **CV**\n   - The corresponding **Job Description (JD)**\n3. Uses **GPT-4** to analyze:\n   - Candidate profile\n   - Role requirements\n   - Interview round context\n4. Generates:\n   - **Tailored interview questions**\n   - **Expected answers**\n   - **A summarized interview prep report**\n5. Sends the report directly to the **hiring team via email (SMTP)**\n\n## 📁 Google Drive Structure\n\n```\n📂 Root Folder\n├── 📁 jd/                          # Stores all job descriptions in PDF format\n│   ├── Backend_Engineer.pdf\n│   ├── Azure_DevOps_Lead.pdf\n│   └── ...\n└── 📄 Positions (Google Sheet)    # Maps Job Role ↔ JD File Link\n```\n\n📝 **Sample Mapping Sheet**:  \n[Positions Sheet](https://docs.google.com/spreadsheets/d/1pW0muHp1NXwh2GiRvGVwGGRYCkcMR7z8NyS9wvSPYjs/edit?usp=sharing)\n\n**Columns:**\n- `Job Role`\n- `Job Description File URL` (pointing to PDF in `jd/` folder)\n\n## 🛠️ How to Set Up\n\n### Step 1: Configure API Integrations\n- ✅ Connect your **OpenAI GPT-4 API Key**\n- ✅ Enable **Google Cloud APIs**:\n  - Google Sheets API (to read job roles)\n  - Google Drive API (to access CV and JD files)\n- ✅ Set up **SMTP** credentials (for email delivery)\n\n### Step 2: Prepare Google Drive & Mapping Sheet\n\n1. Create a root folder on Google Drive  \n2. Inside the root folder:\n   - Create a folder named `/jd/` and upload all **job descriptions (PDFs)**  \n3. Create a **Google Sheet** named `Positions` with the following format:\n\n```\n| Job Role                     | Job Description File URL                   |\n|-----------------------------|--------------------------------------------|\n| Azure DevOps Engineer        | https://drive.google.com/xxx/jd1.pdf       |\n| Full-Stack Developer (.NET)  | https://drive.google.com/xxx/jd2.pdf       |\n```\n\n### Step 3: Build the Application Form\n\nUse any form tool (e.g., Typeform, Tally, or custom HTML) that collects:\n- 📎 Resume file (PDF)\n- 🧾 Job Role (dropdown)\n- 🔄 Interview Round (dropdown)\n\n### Step 4: Resume & JD Extraction\n\n- 🔍 Use `Extract from PDF` to parse the resume content\n- 📄 Retrieve the JD link from the `Positions` sheet based on the selected Job Role\n- 🔗 Use `Download file` to pull the PDF for processing\n\n### Step 5: Analyze with GPT-4\n\n- Run both Resume and JD through a **Profile Analyzer Agent** (GPT-4 with JSON output)\n- Merge results\n- Add manual input or mapping for the **Interview Round metadata**\n\n### Step 6: Generate Interview Report\n\n- Use a second GPT-4 agent (e.g., `HR Expert Agent`) to:\n  - Generate 6–8 tailored interview questions\n  - Include expected answers and rationale\n\n### Step 7: Deliver Final Report\n\n- Format the content as:\n  - 📄 PDF (optional)\n  - 📨 Email body\n- Send the report to the **recruiter, hiring manager, or interviewer** via **SMTP**\n\n## ✅ Requirements\n\n- 🔑 **OpenAI GPT-4 API Key**\n- 📁 Google Drive (for resume and JD storage)\n- 📊 Google Sheet (job role mapping)\n- 📬 SMTP credentials (host, username, password)\n- 🧰 n8n self-hosted or cloud instance with:\n  - PDF Parser\n  - Google Sheets node\n  - HTTP Download node\n  - Email node\n\n## ✏️ How to Customize the Workflow\n\n| Part                        | Customization Options                                      |\n|----------------------------|-------------------------------------------------------------|\n| **Form UI**                | Modify the design, dropdown options, or input validations   |\n| **Job Description Source** | Replace Google Sheet with Notion, Airtable, or database     |\n| **Interview Metadata**     | Add job level, region, or language preference               |\n| **AI Prompt Tuning**       | Adjust prompt phrasing or temperature in GPT nodes          |\n| **Report Format**          | Generate PDF instead of email body using PDF node           |\n| **Delivery Method**        | Add internal HR portal webhook or generate downloadable link |",
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            "content": "# 🤖 Smart Interview Assistant: Tailored Questions Based on CV, JD, and Round\n\n## 📌 Who’s it for\nThis workflow is designed for:\n- **Recruiters** and **Talent Acquisition Specialists** who want to automate candidate interview prep.\n- **Hiring Managers** conducting multiple interviews and needing personalized question sets.\n- **Technical Interviewers** who want to save time and be well-prepared with relevant questions.\n\n## ⚙️ How it works / What it does\nThe Smart Interview Assistant automates the interview preparation process in a few clicks:\n\n1. Accepts:\n   - **Multiple resumes (PDFs)**\n   - **Selected job role**\n   - **Chosen interview round**\n2. Extracts structured data from:\n   - The candidate’s **CV**\n   - The corresponding **Job Description (JD)**\n3. Uses **GPT-4** to analyze:\n   - Candidate profile\n   - Role requirements\n   - Interview round context\n4. Generates:\n   - **Tailored interview questions**\n   - **Expected answers**\n   - **A summarized interview prep report**\n5. Sends the report directly to the **hiring team via email (SMTP)**\n\n## 📁 Google Drive Structure\n\n```\n📂 Root Folder\n├── 📁 jd/                          # Stores all job descriptions in PDF format\n│   ├── Backend_Engineer.pdf\n│   ├── Azure_DevOps_Lead.pdf\n│   └── ...\n└── 📄 Positions (Google Sheet)    # Maps Job Role ↔ JD File Link\n```\n\n📝 **Sample Mapping Sheet**:  \n[Positions Sheet](https://docs.google.com/spreadsheets/d/1pW0muHp1NXwh2GiRvGVwGGRYCkcMR7z8NyS9wvSPYjs/edit?usp=sharing)\n\n**Columns:**\n- `Job Role`\n- `Job Description File URL` (pointing to PDF in `jd/` folder)\n\n## 🛠️ How to Set Up\n\n### Step 1: Configure API Integrations\n- ✅ Connect your **OpenAI GPT-4 API Key**\n- ✅ Enable **Google Cloud APIs**:\n  - Google Sheets API (to read job roles)\n  - Google Drive API (to access CV and JD files)\n- ✅ Set up **SMTP** credentials (for email delivery)\n\n### Step 2: Prepare Google Drive & Mapping Sheet\n\n1. Create a root folder on Google Drive  \n2. Inside the root folder:\n   - Create a folder named `/jd/` and upload all **job descriptions (PDFs)**  \n3. Create a **Google Sheet** named `Positions` with the following format:\n\n```\n| Job Role                     | Job Description File URL                   |\n|-----------------------------|--------------------------------------------|\n| Azure DevOps Engineer        | https://drive.google.com/xxx/jd1.pdf       |\n| Full-Stack Developer (.NET)  | https://drive.google.com/xxx/jd2.pdf       |\n```\n\n### Step 3: Build the Application Form\n\nUse any form tool (e.g., Typeform, Tally, or custom HTML) that collects:\n- 📎 Resume file (PDF)\n- 🧾 Job Role (dropdown)\n- 🔄 Interview Round (dropdown)\n\n### Step 4: Resume & JD Extraction\n\n- 🔍 Use `Extract from PDF` to parse the resume content\n- 📄 Retrieve the JD link from the `Positions` sheet based on the selected Job Role\n- 🔗 Use `Download file` to pull the PDF for processing\n\n### Step 5: Analyze with GPT-4\n\n- Run both Resume and JD through a **Profile Analyzer Agent** (GPT-4 with JSON output)\n- Merge results\n- Add manual input or mapping for the **Interview Round metadata**\n\n### Step 6: Generate Interview Report\n\n- Use a second GPT-4 agent (e.g., `HR Expert Agent`) to:\n  - Generate 6–8 tailored interview questions\n  - Include expected answers and rationale\n\n### Step 7: Deliver Final Report\n\n- Format the content as:\n  - 📄 PDF (optional)\n  - 📨 Email body\n- Send the report to the **recruiter, hiring manager, or interviewer** via **SMTP**\n\n## ✅ Requirements\n\n- 🔑 **OpenAI GPT-4 API Key**\n- 📁 Google Drive (for resume and JD storage)\n- 📊 Google Sheet (job role mapping)\n- 📬 SMTP credentials (host, username, password)\n- 🧰 n8n self-hosted or cloud instance with:\n  - PDF Parser\n  - Google Sheets node\n  - HTTP Download node\n  - Email node\n\n## ✏️ How to Customize the Workflow\n\n| Part                        | Customization Options                                      |\n|----------------------------|-------------------------------------------------------------|\n| **Form UI**                | Modify the design, dropdown options, or input validations   |\n| **Job Description Source** | Replace Google Sheet with Notion, Airtable, or database     |\n| **Interview Metadata**     | Add job level, region, or language preference               |\n| **AI Prompt Tuning**       | Adjust prompt phrasing or temperature in GPT nodes          |\n| **Report Format**          | Generate PDF instead of email body using PDF node           |\n| **Delivery Method**        | Add internal HR portal webhook or generate downloadable link |"
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                },
                {
                  "id": "9baaec07-ddaa-4d10-a80f-33a0dd200fcc",
                  "name": "Technical/Functional Interview",
                  "type": "string",
                  "value": "Purpose: Evaluate the candidate’s core technical or functional skills required for the job.  Conducted by: Team members, senior engineers, or functional managers.  Focus Areas: \t•\tRole-specific skills (e.g., coding, data analysis, sales strategy) \t•\tProblem-solving and past project experience \t•\tUnderstanding of tools, frameworks, or domain knowledge \t•\tScenario-based or behavioral questions related to their role  Goal for Agent: Generate targeted, skill-assessing questions with a mix of practical scenarios and behavioral prompts."
                },
                {
                  "id": "672e2623-81b8-4e2a-959d-3e1ec9886cf8",
                  "name": "Managerial/Team Fit Interview",
                  "type": "string",
                  "value": "Purpose: Assess collaboration skills, ownership, team dynamics, and alignment with team culture.  Conducted by: Hiring Manager or Team Lead.  Focus Areas: \t•\tCommunication and stakeholder management \t•\tDecision-making and conflict resolution \t•\tPast team experiences and leadership style \t•\tCareer growth and adaptability  Goal for Agent: Generate behavioral and situational questions that test emotional intelligence, leadership, and problem ownership."
                },
                {
                  "id": "516ca9b8-de86-41a8-b398-f14e746ed414",
                  "name": "Final Interview (Executive/Leadership Round)",
                  "type": "string",
                  "value": "Purpose: Validate overall fit with company values, long-term potential, and alignment with business goals. May include negotiation.  Conducted by: Director, VP, or C-level executives.  Focus Areas: \t•\tVision alignment and strategic thinking \t•\tLong-term career goals and role alignment \t•\tBusiness acumen and cross-functional collaboration \t•\tSalary/compensation discussion (if applicable)  Goal for Agent: Generate questions around strategic vision, values alignment, and leadership potential."
                }
              ]
            }
          },
          "typeVersion": 3.4
        },
        {
          "id": "9634847d-288d-4c09-824d-b0a391ace376",
          "name": "Merge",
          "type": "n8n-nodes-base.merge",
          "position": [
            544,
            2112
          ],
          "parameters": {},
          "typeVersion": 3.2
        },
        {
          "id": "5867643e-e996-43bd-b025-ba4ad6308289",
          "name": "Transform output",
          "type": "n8n-nodes-base.code",
          "position": [
            768,
            2112
          ],
          "parameters": {
            "jsCode": "// Raw merged input (first N items are candidate profiles, last item is JD)\nconst items = $input.all();\n\n// Extract job description (assuming it's the last element)\nconst jdItem = items[items.length - 1];\nconst job_description = jdItem.json.text;\n// Prepare output array\nconst results = [];\n\n// Loop through all items except the last one (JD)\nfor (let i = 0; i < items.length - 1; i++) {\n  const candidate = items[i].json.output;\n  results.push({\n    json: {\n      candidate_profile: candidate,\n      job_description: job_description,\n      applied_position: $('Application form').first().json['Job Role'],\n      interview_round: $('Application form').first().json['Interview Round']\n    }\n  });\n}\n\nreturn results;"
          },
          "typeVersion": 2
        },
        {
          "id": "61e4c089-30a5-4b93-af09-75f2c68d6fb6",
          "name": "Structured Output Parser",
          "type": "@n8n/n8n-nodes-langchain.outputParserStructured",
          "position": [
            1568,
            2288
          ],
          "parameters": {
            "jsonSchemaExample": "{\n  \"candidate_summary\": {\n    \"name\": \"string\",\n    \"position_applied\": \"string\",\n    \"years_of_experience\": \"number\",\n    \"key_skills\": [\"string\"],\n    \"highlights\": \"string\"\n  },\n  \"job_description_summary\": {\n    \"role_title\": \"string\",\n    \"team\": \"string\",\n    \"core_requirements\": [\"string\"]\n  },\n  \"interview_round\": {\n    \"round_type\": \"string\",\n    \"round_purpose\": \"string\",\n    \"conducted_by\": \"string\",\n    \"focus_areas\": [\"string\"]\n  },\n  \"interview_questions\": [\n    {\n      \"question\": \"string\",\n      \"expected_answer\": \"string\"\n    }\n  ]\n}"
          },
          "typeVersion": 1.3
        },
        {
          "id": "530851ff-f0c3-47a1-abdd-01d13f1875e4",
          "name": "Build interview prep report",
          "type": "n8n-nodes-base.code",
          "position": [
            1984,
            2112
          ],
          "parameters": {
            "mode": "runOnceForEachItem",
            "jsCode": "const data = $json.output;\n\nconst candidate = data.candidate_summary;\nconst jd = data.job_description_summary;\nconst round = data.interview_round;\nconst questions = data.interview_questions;\n\nlet html = `\n  <html>\n    <head>\n      <style>\n        body { font-family: Arial, sans-serif; line-height: 1.5; padding: 20px; }\n        h1, h2 { color: #2c3e50; }\n        h3 { color: #34495e; }\n        ul { padding-left: 20px; }\n        .section { margin-bottom: 25px; }\n        .question { margin-bottom: 15px; }\n        .q-label { font-weight: bold; color: #2c3e50; }\n        .a-label { font-style: italic; color: #7f8c8d; }\n      </style>\n    </head>\n    <body>\n      <h1>Interview Prep Report</h1>\n\n      <div class=\"section\">\n        <h2>Candidate Summary</h2>\n        <p><strong>Name:</strong> ${candidate.name}</p>\n        <p><strong>Position Applied:</strong> ${candidate.position_applied}</p>\n        <p><strong>Experience:</strong> ${candidate.years_of_experience}</p>\n        <p><strong>Highlights:</strong> ${candidate.highlights}</p>\n        <p><strong>Key Skills:</strong></p>\n        <ul>${candidate.key_skills.map(skill => `<li>${skill}</li>`).join('')}</ul>\n      </div>\n\n      <div class=\"section\">\n        <h2>Job Description Summary</h2>\n        <p><strong>Role:</strong> ${jd.role_title}</p>\n        <p><strong>Team:</strong> ${jd.team}</p>\n        <p><strong>Requirements:</strong></p>\n        <ul>${jd.core_requirements.map(req => `<li>${req}</li>`).join('')}</ul>\n      </div>\n\n      <div class=\"section\">\n        <h2>Interview Round</h2>\n        <p><strong>Round Type:</strong> ${round.round_type}</p>\n        <p><strong>Purpose:</strong> ${round.round_purpose}</p>\n        <p><strong>Conducted By:</strong> ${round.conducted_by}</p>\n        <p><strong>Focus Areas:</strong></p>\n        <ul>${round.focus_areas.map(item => `<li>${item}</li>`).join('')}</ul>\n      </div>\n\n      <div class=\"section\">\n        <h2>Interview Questions</h2>\n        ${questions.map((q, index) => `\n          <div class=\"question\">\n            <p class=\"q-label\">Q${index + 1}: ${q.question}</p>\n            <p class=\"a-label\">Expected Answer: ${q.expected_answer}</p>\n          </div>\n        `).join('')}\n      </div>\n    </body>\n  </html>\n`;\n\nreturn { json: { html }}"
          },
          "typeVersion": 2
        },
        {
          "id": "2e188bfa-db80-4493-9ec3-6853ead3e3c1",
          "name": "Send interview prep report to hiring team",
          "type": "n8n-nodes-base.emailSend",
          "position": [
            2208,
            2112
          ],
          "webhookId": "bb7abbfe-9fd9-495c-8a6c-89701389b4d6",
          "parameters": {
            "html": "={{ $json.html }}",
            "options": {},
            "subject": "=Interview Preparation Guide for [{{ $('Interview Expert Agent').item.json.output.candidate_summary.name }}] – [{{ $('Interview Expert Agent').item.json.output.candidate_summary.position_applied }}]",
            "toEmail": "user@example.com",
            "fromEmail": "no-reply@example.com"
          },
          "credentials": {
            "smtp": {
              "id": "credential-id",
              "name": "smtp Credential"
            }
          },
          "typeVersion": 2.1
        },
        {
          "id": "3e49e6fa-0dab-4f73-bd7f-8f6473d0559c",
          "name": "Sticky Note",
          "type": "n8n-nodes-base.stickyNote",
          "position": [
            -336,
            2128
          ],
          "parameters": {
            "width": 780,
            "height": 304,
            "content": "## 2.2. Download selected job description\n- Get position name & job description URL from google sheet based on candidate application form\n- Download job description file from Google Drive\n- Extract data from job description PDF file to be used in the next steps"
          },
          "typeVersion": 1
        },
        {
          "id": "aabd3e08-605e-4fe2-b687-78eccefc1103",
          "name": "Sticky Note2",
          "type": "n8n-nodes-base.stickyNote",
          "position": [
            -336,
            1648
          ],
          "parameters": {
            "width": 780,
            "height": 432,
            "content": "## 2.1. Candidate profile analyzer\n- Extract candidate information from PDF file\n- Transform candidate information to expected JSON format for later use (send message, add to evaluation sheet)"
          },
          "typeVersion": 1
        },
        {
          "id": "17f5f597-3bf3-4314-82a8-67fc654ac144",
          "name": "Sticky Note5",
          "type": "n8n-nodes-base.stickyNote",
          "position": [
            512,
            1952
          ],
          "parameters": {
            "width": 652,
            "height": 336,
            "content": "## 3. Prepare material for Interview Expert Agent\n- Candidate profile\n- Applied position job description\n- Interview round (description, target...)"
          },
          "typeVersion": 1
        },
        {
          "id": "84d88fdb-f333-45c9-94ae-eb5545b2087c",
          "name": "Interview Expert Agent",
          "type": "@n8n/n8n-nodes-langchain.chainLlm",
          "position": [
            1376,
            2112
          ],
          "parameters": {
            "text": "=Generate interview prep document based on the following:\n- Applied Position: {{ $('Transform output').item.json.applied_position }}\n- Job Description: {{ $('Transform output').item.json.job_description }}\n- Candidate Profile: {{ $('Transform output').item.json.candidate_profile.toJsonString() }}\n- Interview Round: {{ $('Transform output').item.json.interview_round }}\n- Interview Round Description: {{ $json[$('Transform output').item.json.interview_round] }}",
            "messages": {
              "messageValues": [
                {
                  "message": "You are an AI Interview Prep Assistant supporting Talent Acquisition (TA) teams. Your job is to generate relevant, role-specific interview questions based on three inputs: the candidate’s profile, the job description (JD), and the selected interview round.  You must first analyze the candidate profile to understand key skills, experiences, and career highlights. Then extract the core requirements and responsibilities from the JD. Based on the specified interview round, adjust the tone, focus, and style of your questions appropriately.  Your output must include: \t•\tA summary of the candidate and applied position, including name, role, years of experience, and notable skills or achievements. \t•\tA summary of the job description, highlighting the core role requirements and expectations. \t•\tThe interview round type, along with its purpose and focus areas. \t•\tA list of 5 to 8 high-quality interview questions, each with a brief explanation of the expected answer or evaluation criteria.  Questions should reflect the intent of the selected round: \t•\tFor screening rounds, keep them light and conversational to verify fit and clarify resume details. \t•\tFor technical or functional rounds, generate questions that evaluate skill depth and practical knowledge. \t•\tFor managerial or leadership rounds, focus on behavior, decision-making, and collaboration. \t•\tFor executive rounds, assess strategic thinking, long-term alignment, and business perspective."
                }
              ]
            },
            "promptType": "define",
            "hasOutputParser": true
          },
          "typeVersion": 1.5
        },
        {
          "id": "64389790-9976-4905-a09b-416dbc445588",
          "name": "Sticky Note6",
          "type": "n8n-nodes-base.stickyNote",
          "position": [
            1216,
            1952
          ],
          "parameters": {
            "width": 652,
            "height": 496,
            "content": "## 4. Interview Expert Agent generate interview prep report\n- Generate interview prep report with candidate summary, interview round note, interview questions.\n- All tailored with input parameters (profile, resume, interview round)"
          },
          "typeVersion": 1
        },
        {
          "id": "f0f1a9f5-92a6-4ac4-8c4c-fc65cbb4d32a",
          "name": "Sticky Note8",
          "type": "n8n-nodes-base.stickyNote",
          "position": [
            1904,
            2048
          ],
          "parameters": {
            "width": 572,
            "height": 224,
            "content": "## 5. Parse the output and send email to hiring team"
          },
          "typeVersion": 1
        },
        {
          "id": "ebf2fcc0-19bd-4d7e-aa49-875952463f52",
          "name": "Sticky Note9",
          "type": "n8n-nodes-base.stickyNote",
          "position": [
            2512,
            1776
          ],
          "parameters": {
            "width": 736,
            "height": 768,
            "content": "![Alt text](https://wisestackai.s3.ap-southeast-1.amazonaws.com/interview-prep-2.png \"Optional title text\")\n![Alt text](https://wisestackai.s3.ap-southeast-1.amazonaws.com/interview-prep-3.png \"Optional title text\")"
          },
          "typeVersion": 1
        }
      ],
      "active": false,
      "pinData": {},
      "settings": {
        "executionOrder": "v1"
      },
      "versionId": "eb364707-bf93-475b-abd4-5105515732a1",
      "connections": {
        "Merge": {
          "main": [
            [
              {
                "node": "Transform output",
                "type": "main",
                "index": 0
              }
            ]
          ]
        },
        "json parser": {
          "ai_outputParser": [
            [
              {
                "node": "Profile Analyzer Agent",
                "type": "ai_outputParser",
                "index": 0
              }
            ]
          ]
        },
        "gpt4-1 model": {
          "ai_languageModel": [
            [
              {
                "node": "Profile Analyzer Agent",
                "type": "ai_languageModel",
                "index": 0
              }
            ]
          ]
        },
        "Download file": {
          "main": [
            [
              {
                "node": "Extract Job Description",
                "type": "main",
                "index": 0
              }
            ]
          ]
        },
        "Extract profile": {
          "main": [
            [
              {
                "node": "Profile Analyzer Agent",
                "type": "main",
                "index": 0
              }
            ]
          ]
        },
        "Get position JD": {
          "main": [
            [
              {
                "node": "Download file",
                "type": "main",
                "index": 0
              }
            ]
          ]
        },
        "gpt-4-1 model 2": {
          "ai_languageModel": [
            [
              {
                "node": "Interview Expert Agent",
                "type": "ai_languageModel",
                "index": 0
              }
            ]
          ]
        },
        "Application form": {
          "main": [
            [
              {
                "node": "Get position JD",
                "type": "main",
                "index": 0
              },
              {
                "node": "Extract profile",
                "type": "main",
                "index": 0
              }
            ]
          ]
        },
        "Transform output": {
          "main": [
            [
              {
                "node": "Interview round metadata",
                "type": "main",
                "index": 0
              }
            ]
          ]
        },
        "Interview Expert Agent": {
          "main": [
            [
              {
                "node": "Build interview prep report",
                "type": "main",
                "index": 0
              }
            ]
          ]
        },
        "Profile Analyzer Agent": {
          "main": [
            [
              {
                "node": "Merge",
                "type": "main",
                "index": 0
              }
            ]
          ]
        },
        "Extract Job Description": {
          "main": [
            [
              {
                "node": "Merge",
                "type": "main",
                "index": 1
              }
            ]
          ]
        },
        "Interview round metadata": {
          "main": [
            [
              {
                "node": "Interview Expert Agent",
                "type": "main",
                "index": 0
              }
            ]
          ]
        },
        "Structured Output Parser": {
          "ai_outputParser": [
            [
              {
                "node": "Interview Expert Agent",
                "type": "ai_outputParser",
                "index": 0
              }
            ]
          ]
        },
        "Build interview prep report": {
          "main": [
            [
              {
                "node": "Send interview prep report to hiring team",
                "type": "main",
                "index": 0
              }
            ]
          ]
        }
      }
    },
    "lastUpdatedBy": 29,
    "workflowInfo": {
      "nodeCount": 26,
      "nodeTypes": {
        "n8n-nodes-base.set": {
          "count": 1
        },
        "n8n-nodes-base.code": {
          "count": 2
        },
        "n8n-nodes-base.merge": {
          "count": 1
        },
        "n8n-nodes-base.emailSend": {
          "count": 1
        },
        "n8n-nodes-base.stickyNote": {
          "count": 10
        },
        "n8n-nodes-base.formTrigger": {
          "count": 1
        },
        "n8n-nodes-base.googleDrive": {
          "count": 1
        },
        "n8n-nodes-base.googleSheets": {
          "count": 1
        },
        "@n8n/n8n-nodes-langchain.agent": {
          "count": 1
        },
        "n8n-nodes-base.extractFromFile": {
          "count": 2
        },
        "@n8n/n8n-nodes-langchain.chainLlm": {
          "count": 1
        },
        "@n8n/n8n-nodes-langchain.lmChatOpenAi": {
          "count": 2
        },
        "@n8n/n8n-nodes-langchain.outputParserStructured": {
          "count": 2
        }
      }
    },
    "status": "published",
    "user": {
      "name": "Trung Tran",
      "username": "trungtran",
      "bio": "Empowering small and medium businesses with smart automation and practical AI, no big tech team required. Youtube channel: youtube.com/@theStackExplorer",
      "verified": true,
      "links": [
        "https://www.automatewith.me"
      ],
      "avatar": "https://gravatar.com/avatar/716d5835286b57fb642fb00972b432f63c72f3a0258e1f6a476ceb02829d21bd?r=pg&d=retro&size=200"
    },
    "nodes": [
      {
        "id": 11,
        "icon": "fa:envelope",
        "name": "n8n-nodes-base.emailSend",
        "codex": {
          "data": {
            "alias": [
              "SMTP",
              "email",
              "human",
              "form",
              "wait",
              "hitl",
              "approval"
            ],
            "resources": {
              "generic": [
                {
                  "url": "https://n8n.io/blog/2021-the-year-to-automate-the-new-you-with-n8n/",
                  "icon": "☀️",
                  "label": "2021: The Year to Automate the New You with n8n"
                },
                {
                  "url": "https://n8n.io/blog/build-your-own-virtual-assistant-with-n8n-a-step-by-step-guide/",
                  "icon": "👦",
                  "label": "Build your own virtual assistant with n8n: A step by step guide"
                }
              ],
              "primaryDocumentation": [
                {
                  "url": "https://docs.n8n.io/integrations/builtin/core-nodes/n8n-nodes-base.sendemail/"
                }
              ],
              "credentialDocumentation": [
                {
                  "url": "https://docs.n8n.io/integrations/builtin/credentials/sendemail/"
                }
              ]
            },
            "categories": [
              "Communication",
              "HITL",
              "Core Nodes"
            ],
            "nodeVersion": "1.0",
            "codexVersion": "1.0",
            "subcategories": {
              "HITL": [
                "Human in the Loop"
              ]
            }
          }
        },
        "group": "[\"output\"]",
        "defaults": {
          "name": "Send Email",
          "color": "#00bb88"
        },
        "iconData": {
          "icon": "envelope",
          "type": "icon"
        },
        "displayName": "Send Email",
        "typeVersion": 2,
        "nodeCategories": [
          {
            "id": 6,
            "name": "Communication"
          },
          {
            "id": 9,
            "name": "Core Nodes"
          },
          {
            "id": 28,
            "name": "HITL"
          }
        ]
      },
      {
        "id": 18,
        "icon": "file:googleSheets.svg",
        "name": "n8n-nodes-base.googleSheets",
        "codex": {
          "data": {
            "alias": [
              "CSV",
              "Sheet",
              "Spreadsheet",
              "GS"
            ],
            "resources": {
              "generic": [
                {
                  "url": "https://n8n.io/blog/love-at-first-sight-ricardos-n8n-journey/",
                  "icon": "❤️",
                  "label": "Love at first sight: Ricardo’s n8n journey"
                },
                {
                  "url": "https://n8n.io/blog/why-business-process-automation-with-n8n-can-change-your-daily-life/",
                  "icon": "🧬",
                  "label": "Why business process automation with n8n can change your daily life"
                },
                {
                  "url": "https://n8n.io/blog/automatically-adding-expense-receipts-to-google-sheets-with-telegram-mindee-twilio-and-n8n/",
                  "icon": "🧾",
                  "label": "Automatically Adding Expense Receipts to Google Sheets with Telegram, Mindee, Twilio, and n8n"
                },
                {
                  "url": "https://n8n.io/blog/supercharging-your-conference-registration-process-with-n8n/",
                  "icon": "🎫",
                  "label": "Supercharging your conference registration process with n8n"
                },
                {
                  "url": "https://n8n.io/blog/creating-triggers-for-n8n-workflows-using-polling/",
                  "icon": "⏲",
                  "label": "Creating triggers for n8n workflows using polling"
                },
                {
                  "url": "https://n8n.io/blog/no-code-ecommerce-workflow-automations/",
                  "icon": "store",
                  "label": "6 e-commerce workflows to power up your Shopify s"
                },
                {
                  "url": "https://n8n.io/blog/migrating-community-metrics-to-orbit-using-n8n/",
                  "icon": "📈",
                  "label": "Migrating Community Metrics to Orbit using n8n"
                },
                {
                  "url": "https://n8n.io/blog/automate-google-apps-for-productivity/",
                  "icon": "💡",
                  "label": "15 Google apps you can combine and automate to increase productivity"
                },
                {
                  "url": "https://n8n.io/blog/your-business-doesnt-need-you-to-operate/",
                  "icon": " 🖥️",
                  "label": "Hey founders! Your business doesn't need you to operate"
                },
                {
                  "url": "https://n8n.io/blog/how-honest-burgers-use-automation-to-save-100k-per-year/",
                  "icon": "🍔",
                  "label": "How Honest Burgers Use Automation to Save $100k per year"
                },
                {
                  "url": "https://n8n.io/blog/how-a-digital-strategist-uses-n8n-for-online-marketing/",
                  "icon": "💻",
                  "label": "How a digital strategist uses n8n for online marketing"
                },
                {
                  "url": "https://n8n.io/blog/why-this-product-manager-loves-workflow-automation-with-n8n/",
                  "icon": "🧠",
                  "label": "Why this Product Manager loves workflow automation with n8n"
                },
                {
                  "url": "https://n8n.io/blog/sending-automated-congratulations-with-google-sheets-twilio-and-n8n/",
                  "icon": "🙌",
                  "label": "Sending Automated Congratulations with Google Sheets, Twilio, and n8n "
                },
                {
                  "url": "https://n8n.io/blog/how-a-membership-development-manager-automates-his-work-and-investments/",
                  "icon": "📈",
                  "label": "How a Membership Development Manager automates his work and investments"
                },
                {
                  "url": "https://n8n.io/blog/aws-workflow-automation/",
                  "label": "7 no-code workflow automations for Amazon Web Services"
                }
              ],
              "primaryDocumentation": [
                {
                  "url": "https://docs.n8n.io/integrations/builtin/app-nodes/n8n-nodes-base.googlesheets/"
                }
              ],
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          "color": "#FFD233"
        },
        "iconData": {
          "icon": "sticky-note",
          "type": "icon"
        },
        "displayName": "Sticky Note",
        "typeVersion": 1,
        "nodeCategories": [
          {
            "id": 9,
            "name": "Core Nodes"
          }
        ]
      },
      {
        "id": 834,
        "icon": "file:code.svg",
        "name": "n8n-nodes-base.code",
        "codex": {
          "data": {
            "alias": [
              "cpde",
              "Javascript",
              "JS",
              "Python",
              "Script",
              "Custom Code",
              "Function"
            ],
            "details": "The Code node allows you to execute JavaScript in your workflow.",
            "resources": {
              "primaryDocumentation": [
                {
                  "url": "https://docs.n8n.io/integrations/builtin/core-nodes/n8n-nodes-base.code/"
                }
              ]
            },
            "categories": [
              "Development",
              "Core Nodes"
            ],
            "nodeVersion": "1.0",
            "codexVersion": "1.0",
            "subcategories": {
              "Core Nodes": [
                "Helpers",
                "Data Transformation"
              ]
            }
          }
        },
        "group": "[\"transform\"]",
        "defaults": {
          "name": "Code"
        },
        "iconData": {
          "type": "file",
          "fileBuffer": "data:image/svg+xml;base64,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"
        },
        "displayName": "Code",
        "typeVersion": 2,
        "nodeCategories": [
          {
            "id": 5,
            "name": "Development"
          },
          {
            "id": 9,
            "name": "Core Nodes"
          }
        ]
      },
      {
        "id": 1119,
        "icon": "fa:robot",
        "name": "@n8n/n8n-nodes-langchain.agent",
        "codex": {
          "data": {
            "alias": [
              "LangChain",
              "Chat",
              "Conversational",
              "Plan and Execute",
              "ReAct",
              "Tools"
            ],
            "resources": {
              "primaryDocumentation": [
                {
                  "url": "https://docs.n8n.io/integrations/builtin/cluster-nodes/root-nodes/n8n-nodes-langchain.agent/"
                }
              ]
            },
            "categories": [
              "AI",
              "Langchain"
            ],
            "subcategories": {
              "AI": [
                "Agents",
                "Root Nodes"
              ]
            }
          }
        },
        "group": "[\"transform\"]",
        "defaults": {
          "name": "AI Agent",
          "color": "#404040"
        },
        "iconData": {
          "icon": "robot",
          "type": "icon"
        },
        "displayName": "AI Agent",
        "typeVersion": 3,
        "nodeCategories": [
          {
            "id": 25,
            "name": "AI"
          },
          {
            "id": 26,
            "name": "Langchain"
          }
        ]
      },
      {
        "id": 1123,
        "icon": "fa:link",
        "name": "@n8n/n8n-nodes-langchain.chainLlm",
        "codex": {
          "data": {
            "alias": [
              "LangChain"
            ],
            "resources": {
              "primaryDocumentation": [
                {
                  "url": "https://docs.n8n.io/integrations/builtin/cluster-nodes/root-nodes/n8n-nodes-langchain.chainllm/"
                }
              ]
            },
            "categories": [
              "AI",
              "Langchain"
            ],
            "subcategories": {
              "AI": [
                "Chains",
                "Root Nodes"
              ]
            }
          }
        },
        "group": "[\"transform\"]",
        "defaults": {
          "name": "Basic LLM Chain",
          "color": "#909298"
        },
        "iconData": {
          "icon": "link",
          "type": "icon"
        },
        "displayName": "Basic LLM Chain",
        "typeVersion": 2,
        "nodeCategories": [
          {
            "id": 25,
            "name": "AI"
          },
          {
            "id": 26,
            "name": "Langchain"
          }
        ]
      },
      {
        "id": 1153,
        "icon": "file:openAiLight.svg",
        "name": "@n8n/n8n-nodes-langchain.lmChatOpenAi",
        "codex": {
          "data": {
            "resources": {
              "primaryDocumentation": [
                {
                  "url": "https://docs.n8n.io/integrations/builtin/cluster-nodes/sub-nodes/n8n-nodes-langchain.lmchatopenai/"
                }
              ]
            },
            "categories": [
              "AI",
              "Langchain"
            ],
            "subcategories": {
              "AI": [
                "Language Models",
                "Root Nodes"
              ],
              "Language Models": [
                "Chat Models (Recommended)"
              ]
            }
          }
        },
        "group": "[\"transform\"]",
        "defaults": {
          "name": "OpenAI Chat Model"
        },
        "iconData": {
          "type": "file",
          "fileBuffer": "data:image/svg+xml;base64,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"
        },
        "displayName": "OpenAI Chat Model",
        "typeVersion": 1,
        "nodeCategories": [
          {
            "id": 25,
            "name": "AI"
          },
          {
            "id": 26,
            "name": "Langchain"
          }
        ]
      },
      {
        "id": 1179,
        "icon": "fa:code",
        "name": "@n8n/n8n-nodes-langchain.outputParserStructured",
        "codex": {
          "data": {
            "alias": [
              "json",
              "zod"
            ],
            "resources": {
              "primaryDocumentation": [
                {
                  "url": "https://docs.n8n.io/integrations/builtin/cluster-nodes/sub-nodes/n8n-nodes-langchain.outputparserstructured/"
                }
              ]
            },
            "categories": [
              "AI",
              "Langchain"
            ],
            "subcategories": {
              "AI": [
                "Output Parsers"
              ]
            }
          }
        },
        "group": "[\"transform\"]",
        "defaults": {
          "name": "Structured Output Parser"
        },
        "iconData": {
          "icon": "code",
          "type": "icon"
        },
        "displayName": "Structured Output Parser",
        "typeVersion": 1,
        "nodeCategories": [
          {
            "id": 25,
            "name": "AI"
          },
          {
            "id": 26,
            "name": "Langchain"
          }
        ]
      },
      {
        "id": 1225,
        "icon": "file:form.svg",
        "name": "n8n-nodes-base.formTrigger",
        "codex": {
          "data": {
            "alias": [
              "table",
              "submit",
              "post"
            ],
            "resources": {
              "generic": [],
              "primaryDocumentation": [
                {
                  "url": "https://docs.n8n.io/integrations/builtin/core-nodes/n8n-nodes-base.formtrigger/"
                }
              ]
            },
            "categories": [
              "Core Nodes"
            ],
            "nodeVersion": "1.0",
            "codexVersion": "1.0",
            "subcategories": {
              "Core Nodes": [
                "Other Trigger Nodes"
              ]
            }
          }
        },
        "group": "[\"trigger\"]",
        "defaults": {
          "name": "On form submission"
        },
        "iconData": {
          "type": "file",
          "fileBuffer": "data:image/svg+xml;base64,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"
        },
        "displayName": "n8n Form Trigger",
        "typeVersion": 3,
        "nodeCategories": [
          {
            "id": 9,
            "name": "Core Nodes"
          }
        ]
      },
      {
        "id": 1235,
        "icon": "file:extractFromFile.svg",
        "name": "n8n-nodes-base.extractFromFile",
        "codex": {
          "data": {
            "alias": [
              "CSV",
              "Spreadsheet",
              "Excel",
              "xls",
              "xlsx",
              "ods",
              "tabular",
              "decode",
              "decoding",
              "Move Binary Data",
              "Binary",
              "File",
              "PDF",
              "JSON",
              "HTML",
              "ICS",
              "iCal",
              "txt",
              "Text",
              "RTF",
              "XML",
              "64",
              "Base64",
              "Convert"
            ],
            "resources": {
              "primaryDocumentation": [
                {
                  "url": "https://docs.n8n.io/integrations/builtin/core-nodes/n8n-nodes-base.extractfromfile/"
                }
              ]
            },
            "categories": [
              "Core Nodes"
            ],
            "nodeVersion": "1.0",
            "codexVersion": "1.0",
            "subcategories": {
              "Core Nodes": [
                "Files",
                "Data Transformation"
              ]
            }
          }
        },
        "group": "[\"input\"]",
        "defaults": {
          "name": "Extract from File"
        },
        "iconData": {
          "type": "file",
          "fileBuffer": "data:image/svg+xml;base64,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"
        },
        "displayName": "Extract from File",
        "typeVersion": 1,
        "nodeCategories": [
          {
            "id": 9,
            "name": "Core Nodes"
          }
        ]
      }
    ],
    "categories": [
      {
        "id": 17,
        "name": "HR"
      },
      {
        "id": 51,
        "name": "Multimodal AI"
      }
    ],
    "image": []
  }
}