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:
- 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.
- 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.
- Consolidation: All the retrieved question-and-answer pairs are compiled into a single, comprehensive research brief.
- 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
- 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.
- Configure the workflow:
- Connect your AI provider (e.g., OpenAI) credentials to the two Language Model nodes (
GPT 5 miniandGPT 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).
- Connect your AI provider (e.g., OpenAI) credentials to the two Language Model nodes (
- 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.
Create Fact-Based Articles from Your Knowledge Sources with Super RAG and GPT-5
This n8n workflow automates the process of generating fact-based articles by leveraging advanced Retrieval Augmented Generation (RAG) techniques with a GPT-5 (or similar) language model. It takes a topic as input, retrieves relevant information from a specified knowledge source (via an HTTP request), processes this information, and then uses an LLM to synthesize it into a structured article.
What it does
This workflow simplifies article generation by:
- Triggering on Form Submission: It starts when a user submits a form, likely providing a topic or query for the article.
- Fetching Knowledge: It makes an HTTP request to an external API (your knowledge source) to retrieve information relevant to the submitted topic.
- Processing Retrieved Data: It then prepares the retrieved data for the language model.
- Looping for Context: It iterates through the fetched data in batches, potentially to manage context windows for the LLM or to process large datasets incrementally.
- Generating Article Content: For each batch of data, it uses an OpenAI Chat Model (acting as a GPT-5 equivalent in this context) within a Basic LLM Chain to generate article content based on the retrieved facts.
- Structuring Output: It uses a Structured Output Parser to ensure the generated content adheres to a predefined format, making the output consistent and easy to consume.
- Aggregating Results: Finally, it combines all the generated content from the batches into a single, comprehensive article.
- Splitting Output: It then splits out the aggregated results, likely to prepare them for further processing or storage.
Prerequisites/Requirements
- n8n Instance: A running n8n instance.
- OpenAI API Key: An API key for OpenAI (or a compatible LLM provider configured as an OpenAI Chat Model).
- External Knowledge Source: An API endpoint for your knowledge base that can respond to HTTP requests with relevant information.
- Form Submission Mechanism: A way to submit data to the n8n Form Trigger (e.g., embedding the form on a website, using a simple HTML form).
Setup/Usage
- Import the Workflow: Download the provided JSON and import it into your n8n instance.
- Configure Credentials:
- OpenAI Chat Model: Configure your OpenAI API key within the "OpenAI Chat Model" node.
- HTTP Request: Update the "HTTP Request" node with the URL and any necessary authentication or parameters for your external knowledge source API.
- Configure Form Trigger:
- Activate the "On form submission" trigger node.
- Note the webhook URL provided by the trigger node. This is where your form submissions should be sent.
- Customize Data Processing (Optional):
- The "Edit Fields (Set)" node can be used to transform or prepare the input data before it's sent to the HTTP Request or the LLM.
- Adjust the "Loop Over Items (Split in Batches)" node's batch size if your knowledge source returns very large or very small chunks of data.
- Modify the "Basic LLM Chain" node's prompt to guide the article generation towards your desired style, tone, and structure.
- Update the "Structured Output Parser" node with the exact JSON schema you expect for your generated articles.
- Activate the Workflow: Once configured, activate the workflow.
- Submit a Form: Submit data to the n8n Form Trigger webhook URL (e.g., a JSON payload with a
topicfield) to initiate the article generation.
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