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Create fact-based articles from your knowledge sources with Super RAG and GPT-5

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Important notice

This workflow is provided as-is. Please review and test before using in production.

1. Workflow Overview

Move beyond generic AI generated content and create articles that are high quality, factually reliable, and aligned with your unique expertise. This template orchestrates a sophisticated "research ...

Best for

  • AI RAG automation workflows
  • Multimodal AI automation workflows
  • advanced n8n builders looking for reusable templates

Tools used

@n8n/n8n-nodes-langchain.chainllm, n8n-nodes-base.set, n8n-nodes-base.formtrigger, @n8n/n8n-nodes-langchain.outputparserstructured, @n8n/n8n-nodes-langchain.lmchatopenai, n8n-nodes-base.splitout, n8n-nodes-base.splitinbatches, n8n-nodes-base.httprequest

Source and attribution

This workflow is cataloged by N8N Workflows and links back to its original n8n.io source page by Guillaume Duvernay.

Original n8n.io source

1.1 Workflow description

Title
Create fact-based articles from your knowledge sources with Super RAG and GPT-5
Workflow name
Create fact-based articles from your knowledge sources with Super RAG and GPT-5

Move beyond generic AI-generated content and create articles that are high-quality, factually reliable, and aligned with your unique expertise. This template orchestrates a sophisticated "research-first" content creation process. Instead of simply asking an AI to write an article from scratch, it first uses an AI planner to break your topic down into logical sub-questions.

It then queries a Super assistant—which you've connected to your own trusted knowledge sources like Notion, Google Drive, or PDFs—to build a comprehensive research brief. Only then is this fact-checked brief handed to a powerful AI writer to compose the final article, complete with source links. This is the ultimate workflow for scaling expert-level content creation.

Who is this for?

  • Content marketers & SEO specialists: Scale the creation of authoritative, expert-level blog posts that are grounded in factual, source-based information.
  • Technical writers & subject matter experts: Transform your complex internal documentation into accessible public-facing articles, tutorials, and guides.
  • Marketing agencies: Quickly generate high-quality, well-researched drafts for clients by connecting the workflow to their provided brand and product materials.

What problem does this solve?

  • Reduces AI "hallucinations": By grounding the entire writing process in your own trusted knowledge base, the AI generates content based on facts you provide, not on potentially incorrect information from its general training data.
  • Ensures comprehensive topic coverage: The initial AI-powered "topic breakdown" step acts like an expert outliner, ensuring the final article is well-structured and covers all key sub-topics.
  • Automates source citation: The workflow is designed to preserve and integrate source URLs from your knowledge base directly into the final article as hyperlinks, boosting credibility and saving you manual effort.
  • Scales expert content creation: It effectively mimics the workflow of a human expert (outline, research, consolidate, write) but in an automated, scalable, and incredibly fast way.

How it works

This workflow follows a sophisticated, multi-step process to ensure the highest quality output:

  1. Decomposition: You provide an article title and guidelines via the built-in form. An initial AI call then acts as a "planner," breaking down the main topic into an array of 5-8 logical sub-questions.
  2. Fact-based research (RAG): The workflow loops through each of these sub-questions and queries your Super assistant. This assistant, which you have pre-configured and connected to your own knowledge sources (Notion pages, Google Drive folders, PDFs, etc.), finds the relevant information and source links for each point.
  3. Consolidation: All the retrieved question-and-answer pairs are compiled into a single, comprehensive research brief.
  4. Final article generation: This complete, fact-checked brief is handed to a final, powerful AI writer (e.g., GPT-5). Its instructions are clear: write a high-quality article using only the provided information and integrate the source links as hyperlinks where appropriate.

Implementing the template

  1. Set up your Super assistant (Prerequisite): First, go to Super, create an assistant, connect it to your knowledge sources (Notion, Drive, etc.), and copy its Assistant ID and your API Token.
  2. Configure the workflow:
    • Connect your AI provider (e.g., OpenAI) credentials to the two Language Model nodes (GPT 5 mini and GPT 5 chat).
    • In the Query Super Assistant (HTTP Request) node, paste your Assistant ID in the body and add your Super API Token for authentication (we recommend using a Bearer Token credential).
  3. Activate the workflow: Toggle the workflow to "Active" and use the built-in form to generate your first fact-checked article!

Taking it further

  • Automate publishing: Connect the final Article result node to a Webflow or WordPress node to automatically create a draft post in your CMS.
  • Generate content in bulk: Replace the Form Trigger with an Airtable or Google Sheet trigger to automatically generate a whole batch of articles from your content calendar.
  • Customize the writing style: Tweak the system prompt in the final New content - Generate the AI output node to match your brand's specific tone of voice, add SEO keywords, or include specific calls-to-action.

1.2 Logical Blocks

This catalog entry is organized from the workflow JSON. The node-level section below shows the executable blocks available for review before importing the template.

2. Block-by-Block Analysis

Block 1 - New content - generate research questions

Type / Role
@n8n/n8n-nodes-langchain.chainLlm - chainLlm
Config choices
Version 1.5

Block 2 - Format question and answer

Type / Role
n8n-nodes-base.set - set
Config choices
Version 3.4

Block 3 - New content - Generate the AI output

Type / Role
@n8n/n8n-nodes-langchain.chainLlm - chainLlm
Config choices
Version 1.5

Block 4 - New article form

Type / Role
n8n-nodes-base.formTrigger - formTrigger
Config choices
Version 2.3

Block 5 - Prepare form values

Type / Role
n8n-nodes-base.set - set
Config choices
Version 3.4

Block 6 - Structured Output Parser

Type / Role
@n8n/n8n-nodes-langchain.outputParserStructured - outputParserStructured
Config choices
Version 1.2

Block 7 - GPT 5 mini

Type / Role
@n8n/n8n-nodes-langchain.lmChatOpenAi - lmChatOpenAi
Config choices
Version 1.2

Block 8 - Split Out Questions

Type / Role
n8n-nodes-base.splitOut - splitOut
Config choices
Version 1

Block 9 - Loop Over Questions

Type / Role
n8n-nodes-base.splitInBatches - splitInBatches
Config choices
Version 3

Block 10 - Query Super Assistant

Type / Role
n8n-nodes-base.httpRequest - httpRequest
Config choices
Version 4.2

Block 11 - GPT 5 chat

Type / Role
@n8n/n8n-nodes-langchain.lmChatOpenAi - lmChatOpenAi
Config choices
Version 1.2

Block 12 - Article result

Type / Role
n8n-nodes-base.set - set
Config choices
Version 3.4

Block 13 - Sticky Note

Type / Role
n8n-nodes-base.stickyNote - stickyNote
Config choices
Version 1

Block 14 - Aggregate research content

Type / Role
n8n-nodes-base.aggregate - aggregate
Config choices
Version 1

Block 15 - Sticky Note1

Type / Role
n8n-nodes-base.stickyNote - stickyNote
Config choices
Version 1

Block 16 - Sticky Note2

Type / Role
n8n-nodes-base.stickyNote - stickyNote
Config choices
Version 1

Block 17 - Sticky Note3

Type / Role
n8n-nodes-base.stickyNote - stickyNote
Config choices
Version 1

Block 18 - Sticky Note4

Type / Role
n8n-nodes-base.stickyNote - stickyNote
Config choices
Version 1

Block 19 - Sticky Note5

Type / Role
n8n-nodes-base.stickyNote - stickyNote
Config choices
Version 1

3. Summary Table

Workflow Create fact-based articles from your knowledge sources with Super RAG and GPT-5
Complexity advanced
Nodes 19
Categories AI RAG, Multimodal AI
Author Guillaume Duvernay
Published 26 Aug 2025

4. Reproducing the Workflow from Scratch

  1. 1. Download the workflow JSON

    Use the JSON export at /data/workflows/7907/7907.json as the source template for this automation.

  2. 2. Import the template into n8n

    Open n8n, import the downloaded JSON, and review each node before activating the workflow.

  3. 3. Configure credentials and variables

    Replace placeholder credentials, API keys, webhook URLs, account IDs, and environment-specific values with your own settings.

  4. 4. Test with sample data

    Run the workflow manually or in a staging workspace, inspect node output, and confirm downstream systems receive the expected data.

  5. 5. Activate and monitor

    Enable the workflow only after testing, then monitor executions, errors, and rate limits during the first production runs.

5. General Notes & Resources

Review imported nodes carefully before activation. This catalog entry is intended to help you inspect the workflow structure, understand required services, and find related templates faster.

Node names, credentials, schedules, webhook paths, and external service limits may need adjustment for your workspace.

Frequently asked questions

What does Create fact-based articles from your knowledge sources with Super RAG and GPT-5 do?

Move beyond generic AI generated content and create articles that are high quality, factually reliable, and aligned with your unique expertise. This template orchestrates a sophisticated "research ...

What do I need before importing this workflow?

Review the workflow JSON, configure any required credentials in n8n, and test the automation in a safe workspace before using it in production.

Can I customize this workflow?

Yes. Use the block-by-block analysis and the downloadable JSON to inspect each node, then adjust credentials, prompts, schedules, filters, or destinations for your AI RAG, Multimodal AI use case.