This article provides a complete, practical walkthrough of the Googledocs Code Create Webhook n8n agent. It connects HTTP Request, Webhook across approximately 1 node(s). Expect a Intermediate setup in 15-45 minutes. One‑time purchase: €29.

What This Agent Does

This agent orchestrates a reliable automation between HTTP Request, Webhook, handling triggers, data enrichment, and delivery with guardrails for errors and rate limits.

It streamlines multi‑step processes that would otherwise require manual exports, spreadsheet cleanup, and repeated API requests. By centralizing logic in n8n, it reduces context switching, lowers error rates, and ensures consistent results across teams.

Typical outcomes include faster lead handoffs, automated notifications, accurate data synchronization, and better visibility via execution logs and optional Slack/Email alerts.

How It Works

The workflow uses standard n8n building blocks like Webhook or Schedule triggers, HTTP Request for API calls, and control nodes (IF, Merge, Set) to validate inputs, branch on conditions, and format outputs. Retries and timeouts improve resilience, while credentials keep secrets safe.

Third‑Party Integrations

• HTTP Request
• Webhook

Import and Use in n8n

1. Open n8n and create a new workflow or collection.
2. Choose Import from File or Paste JSON.
3. Paste the JSON below, then click Import.
4. Show n8n JSON

   Title:  
   Automated Exam Question Creation Using AI and n8n: A Step-by-Step Breakdown  
   
   Meta Description:  
   Discover how an n8n-powered workflow integrates Google Docs, OpenAI, Qdrant, and Google Gemini to automatically generate open and multiple-choice exam questions from educational content—streamlining assessment creation for educators.
   
   Keywords:  
   n8n workflow, AI exam creation, automated test generation, Google Docs integration, OpenAI embeddings, Google Gemini LLM, vector database Qdrant, educational AI tools, RAG pipeline, assessment automation
   
   Third-Party APIs Used:
   
   - Google Docs API (via n8n Google Docs node)
   - Google Gemini / PaLM API (via LangChain + Google Gemini Model node)
   - OpenAI API (Embeddings endpoint)
   - Qdrant Vector Database API
   - Google Sheets API (via n8n Google Sheets node)
   
   Article:
   
   ---
   
   # Transforming Educational Assessments: Automated Exam Generation with AI and n8n
   
   Creating meaningful and high-quality exam questions is one of the most time-consuming tasks for educators. From reading the source material to designing multiple-choice questions that assess various cognitive levels, the process often takes hours per document. But what if you could automate it?
   
   Thanks to a powerful integration of AI technologies inside the n8n automation platform, educators now have access to an automated workflow that converts Google Docs into comprehensive sets of both open-ended and closed (multiple-choice) questions. Let’s explore exactly how this workflow operates and the technologies that bring it to life.
   
   ---
   
   ## Purpose: Automating Exam Question Generation
   
   The primary goal of the workflow is to analyze document-based content—such as lecture notes, academic articles, or training manuals—stored in Google Docs and automatically generate:
   
   - 10 open-ended questions that test comprehension, analysis, and inference.
   - 10 multiple-choice questions, each with 1 correct and 3 plausible incorrect answers.
   
   It does so using AI models for natural language understanding, vector databases for semantic search, and interfaces to commonly used document and spreadsheet systems.
   
   ---
   
   ## Step 1: Document Preparation from Google Docs
   
   The process begins with the user triggering the n8n workflow. A designated Google Doc, containing the reference content, is fetched using the Google Docs API. A custom code node then converts the rich text and formatting from Google Docs into clean, structured Markdown. This markdown serves as the input for the AI models downstream.
   
   ---
   
   ## Step 2: Embedding Knowledge into a Vector Database
   
   To enable intelligent question-answering later, the markdown-formatted content is turned into embeddings using OpenAI's Embeddings API. These high-dimensional vector representations are then stored into a Qdrant vector database collection.
   
   Qdrant acts as a semantic memory, allowing the AI to “retrieve” precise pieces of content based on context when generating answers or refining question quality. Collections are refreshed and updated dynamically according to each document session.
   
   ---
   
   ## Step 3: Generating Open-Ended Questions
   
   Using the Google Gemini “Pro” or “Flash” large language models (LLMs), the converted markdown content is fed into an AI chain with a system prompt that instructs the model to generate 10 open-ended, thought-provoking questions.
   
   These questions avoid yes/no answers and instead span various levels of Bloom's taxonomy—ranging from factual recall to application to analysis. A parser ensures the output format is consistent and each question is treated as a clean, standalone item.
   
   Then, each question is answered by a separate AI retrieval-based chain. The answer is derived exclusively by retrieving relevant content from the Qdrant vector store using Retrieval-Augmented Generation (RAG). This ensures that answers are traceable and grounded in the original source material.
   
   Both questions and answers are stored in a Google Sheet (“Open Questions” tab) for easy review and integration into teaching tools.
   
   ---
   
   ## Step 4: Generating Multiple-Choice Questions
   
   Parallel to the open-question pipeline, a similar chain is dedicated to crafting multiple-choice items. The goal here is clarity, balance, and curriculum alignment:
   
   - Each of the 10 questions includes four options (A–D).
   - Only one answer is correct, and it must be inferred or verifiably sourced from the content in Qdrant (again using RAG).
   - The three distractors are intelligently generated to be plausible but ultimately incorrect, making the test both fair and challenging.
   
   The answers are parsed into structured JSON format and then recorded into the Google Sheet (“Closed Questions” tab), ready for direct use in LMS systems or quizzes.
   
   ---
   
   ## Why This Workflow Matters
   
   This n8n workflow considerably enhances the efficiency and consistency of assessment generation:
   
   - ✅ Saves time: Educators focus on teaching, not formatting questions.
   - ✅ Ensures quality: AI promotes varied question types with cognitive depth.
   - ✅ Boosts reliability: Vector database retrieval makes AI answers evidence-based.
   - ✅ Easy to adapt: Change the Google Doc, rerun the workflow, done.
   - ✅ Transparent: All outputs are saved in an editable, shareable Google Sheet.
   
   ---
   
   ## Under the Hood: Tech Stack Overview
   
   Here’s a quick review of the tools and APIs that power this smart automation:
   
   - 📄 Google Docs API – Retrieves educational content in rich text format.
   - ✍️ Custom JavaScript Node – Converts structured Docs content into markdown.
   - 💡 OpenAI API – Generates vector embeddings of the text using the Embeddings endpoint.
   - 🧠 Qdrant Vector DB – Stores and retrieves semantic chunks of article data.
   - 🗣️ Google Gemini (formerly PaLM) API – Language model that creates and answers test questions.
   - ➕ Google Sheets API – Saves the structured questions and answers for downstream use.
   - 🔁 n8n Automation – Orchestrates and connects all these moving parts with no code required.
   
   ---
   
   ## Final Thoughts
   
   AI is no longer a futuristic concept in education—it’s a productivity tool ready to be adopted now. This n8n workflow represents a powerful example of how low-code tools and LLMs can revolutionize specific academic tasks.
   
   Want to tailor it for your own subject or institution? Since the flow is modular, you can easily adapt it to support different types of content, disciplines, question formats, or languages.
   
   With this kind of automation, the future of smarter, scalable education is already here.
   
   —
   
   Want help integrating this automation into your classroom or LMS? Contact our team of AI workflow experts to get started.

5. Set credentials for each API node (keys, OAuth) in Credentials.
6. Run a test via Execute Workflow. Inspect Run Data, then adjust parameters.
7. Enable the workflow to run on schedule, webhook, or triggers as configured.

Tips: keep secrets in credentials, add retries and timeouts on HTTP nodes, implement error notifications, and paginate large API fetches.

Validation: use IF/Code nodes to sanitize inputs and guard against empty payloads.

Why Automate This with AI Agents

AI‑assisted automations offload repetitive, error‑prone tasks to a predictable workflow. Instead of manual copy‑paste and ad‑hoc scripts, your team gets a governed pipeline with versioned state, auditability, and observable runs.

n8n’s node graph makes data flow transparent while AI‑powered enrichment (classification, extraction, summarization) boosts throughput and consistency. Teams reclaim time, reduce operational costs, and standardize best practices without sacrificing flexibility.

Compared to one‑off integrations, an AI agent is easier to extend: swap APIs, add filters, or bolt on notifications without rewriting everything. You get reliability, control, and a faster path from idea to production.

Best Practices

• Credentials: restrict scopes and rotate tokens regularly.
• Resilience: configure retries, timeouts, and backoff for API nodes.
• Data Quality: validate inputs; normalize fields early to reduce downstream branching.
• Performance: batch records and paginate for large datasets.
• Observability: add failure alerts (Email/Slack) and persistent logs for auditing.
• Security: avoid sensitive data in logs; use environment variables and n8n credentials.

FAQs

Can I swap integrations later? Yes. Replace or add nodes and re‑map fields without rebuilding the whole flow.

How do I monitor failures? Use Execution logs and add notifications on the Error Trigger path.

Does it scale? Use queues, batching, and sub‑workflows to split responsibilities and control load.

Is my data safe? Keep secrets in Credentials, restrict token scopes, and review access logs.