Create fact-based articles from knowledge sources with Lookio and OpenAI GPT
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 Lookio assistant—which you've connected to your own trusted knowledge base of uploaded documents—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 Lookio assistant. This assistant, which you have pre-configured by uploading your own documents, 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-4o). Its instructions are clear: write a high-quality article using only the provided information and integrate the source links as hyperlinks where appropriate.
Building your own RAG pipeline VS using Lookio or alternative tools
Building a RAG system natively within n8n offers deep customization, but it requires managing a toolchain for data processing, text chunking, and retrieval optimization.
An alternative is to use a managed service like Lookio, which provides RAG functionality through an API. This approach abstracts the backend infrastructure for document ingestion and querying, trading the granular control of a native build for a reduction in development and maintenance tasks.
Implementing the template
1. Set up your Lookio assistant (Prerequisite):
Lookio is a platform for building intelligent assistants that leverage your organization's documents as a dedicated knowledge base.
- First, sign up at Lookio. You'll get 50 free credits to get started.
- Upload the documents you want to use as your knowledge base.
- Create a new assistant and then generate an API key.
- Copy your Assistant ID and your API Key for the next step.
2. Configure the workflow:
- Connect your AI provider (e.g., OpenAI) credentials to the two Language Model nodes.
- In the Query Lookio Assistant (HTTP Request) node, paste your Assistant ID in the body and add your Lookio API Key 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.
Create Fact-Based Articles from Knowledge Sources with Lookio and OpenAI GPT
This n8n workflow automates the generation of fact-based articles by integrating knowledge sources (via HTTP requests) with OpenAI's GPT models, and then structuring the output. It allows for dynamic article creation based on user-defined topics or data.
What it does
This workflow simplifies the process of generating structured articles by:
- Triggering on Form Submission: Initiates the workflow when a form is submitted, likely containing the topic or prompt for the article generation.
- Fetching Knowledge Sources: Makes an HTTP request to an external API (presumably Lookio or a similar knowledge base) to retrieve relevant information based on the input.
- Preparing Data for LLM: Transforms and sets the fetched data into a format suitable for the Language Model.
- Looping Over Data (Optional Batches): If multiple items are processed, it can loop over them in batches for efficient processing.
- Generating Article Content with OpenAI GPT: Uses an OpenAI Chat Model within a Basic LLM Chain to generate article content based on the knowledge sources and a defined prompt.
- Structuring Output: Parses the generated content using a Structured Output Parser to ensure the article adheres to a predefined JSON structure (e.g., title, sections, facts).
- Aggregating Results: Combines the structured outputs from multiple generations (if looping) into a single collection.
- Splitting Out Data: Separates the aggregated data into individual items for further processing or storage.
Prerequisites/Requirements
- n8n Instance: A running n8n instance to import and execute the workflow.
- OpenAI API Key: An API key for OpenAI to access their GPT models. This will need to be configured as an n8n credential.
- External Knowledge Source API: Access to an API that can provide factual information (e.g., Lookio, a custom database, or another web service). The HTTP Request node will need to be configured with the appropriate endpoint and authentication.
Setup/Usage
- Import the Workflow: Download the provided JSON and import it into your n8n instance.
- Configure Credentials:
- Set up an OpenAI API Key credential in n8n.
- Configure any necessary credentials for your external knowledge source API within the "HTTP Request" node.
- Configure the "On form submission" Trigger:
- Define the form fields that will provide input for your article generation (e.g.,
topic,keywords).
- Define the form fields that will provide input for your article generation (e.g.,
- Configure the "HTTP Request" Node:
- Update the
URLandMethodto point to your knowledge source API. - Map any input from the trigger to the request body or query parameters to fetch relevant data.
- Update the
- Configure the "Edit Fields (Set)" Node:
- Ensure this node correctly transforms the data from the HTTP Request into a format expected by the LLM Chain. You might need to extract specific fields or combine them into a single prompt variable.
- Configure the "Basic LLM Chain" and "OpenAI Chat Model" Nodes:
- Select your OpenAI credential.
- Define the
Model(e.g.,gpt-4,gpt-3.5-turbo). - Craft the
System MessageandUser Messagewithin the LLM Chain to guide the GPT model on how to generate the article, referencing the data from previous nodes.
- Configure the "Structured Output Parser" Node:
- Define the
JSON Schemathat describes the desired structure of your output article (e.g.,{ "type": "object", "properties": { "title": { "type": "string" }, "sections": { "type": "array", "items": { "type": "object", "properties": { "heading": { "type": "string" }, "content": { "type": "string" } } } } } }). This is crucial for consistent article formatting.
- Define the
- Activate the Workflow: Once configured, activate the workflow. You can then test it by submitting the form defined in the trigger.
This workflow provides a powerful foundation for automating content creation, ensuring articles are not only well-written but also grounded in factual information from your chosen knowledge sources.
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