How to set up a Minimal RAG system in n8n for content smarter internal linking
TL;DR
You don’t need a vector database to give your AI agent context. In this guide, I show you how to build a minimal RAG setup using n8n and a Google Sheet to automate internal link suggestions in blog content. It’s fast to implement, SEO-friendly, and perfect for proving value before scaling.
New to AI content strategy? Content drives revenue! See how AI helps you scale it profitably lays the foundation for why tools like minimal RAG matter.
This guide is for you if you’ve built or are building a content-generating AI agent that creates articles for your business. It writes, formats, maybe even schedules.
But here’s one thing it probably doesn’t do yet: suggest relevant internal links.
Without internal linking, your SEO performance suffers, and users lose valuable context.
The good news? Even if your internal links aren’t stored in a vector database, and you have zero experience with semantic embeddings (yet), you can still fix it.
In this post, I’ll show you how to build a minimal RAG (Retrieval-Augmented Generation) system using n8n that powers smart internal link suggestions using just a spreadsheet.
This minimal RAG guide is ideal for:
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MVPs, to help you demonstrate the value of RAG to stakeholders
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Lean teams who want to introduce SEO-conscious automation into their content process without overengineering anything
What is minimal RAG?
If you’re new to the concept of Retrieval-Augmented Generation (RAG), start with our Minimal RAG 101 guide — it breaks down what RAG is, why agents need context, and how Minimal RAG compares to a full RAG setup using vector databases.
In short: Full RAG systems involve vector databases, semantic embeddings, and complex infrastructure. It is powerful, but time-intensive to set up, and now always necessary.
With Minimal RAG, you skip the heavy lift. Instead, you use structured metadata (like titles, tags, and summaries) to guide your AI agent , making it fast to implement and perfect for MVPs.
In this article, we’ll show you exactly how we applied Minimal RAG in n8n to automate internal link suggestions. Here, no vector DB is required.
Why is Minimal RAG a good way to start giving context to your agents?
Let’s start a little bit technical without being to technical. Retrieval-Augmented Generation enhances AI output by injecting information relevant to your business and relevant to the agent’s task before generation — so it doesn’t rely solely on the model’s training data.
Pre-trained AI models don’t know:
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Your actual product details
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Your nuanced industry insights
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Your blog archive or internal links
Minimal RAG fills that gap. It gives your agent just enough context to be useful, boosting SEO, guiding better output, and improving UX without heavy lifting.
The problem we are trying to solve: Optimise SEO by adding relevant internal linking
If SEO matters to you, internal linking isn’t optional: it’s essential. And if you’ve already made it a priority, you probably know the struggle: finding the right pages to link to, figuring out how to naturally weave them into the content, and doing it consistently across every post.
Internal links:
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Help Google crawl and index your site more effectively
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Distribute authority across your pages (known as link equity)
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Reinforce topical authority for your domain
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Improve user experience by guiding visitors to related content
Most SEO best practices recommend including around 6 internal links for articles averaging 1200 words. But picking the right ones and phrasing them well can be surprisingly time-consuming and very often it becomes inconsistent. Especially when you’re working at scale or under tight deadlines.
By introducing a Minimal RAG system, you can « automate » this process. Your generative agent can reference existing content intelligently — boosting SEO and content quality in every post.
🛠️ What we’ll build
Today I want to show you how to solve this problem effectively using n8n and a minimal RAG.
In this example — one of the MVPs we set up for a client — we designed a multi-agent system, where one of the agents was responsible for generating a blog brief.
As part of the AI generated brief, the client wanted to receive recommendations for internal links to include in the eventual blog post. In fact, they didn’t want the agent to just automatically insert links during content generation later in the process. Instead, they wanted to review the suggested links first, understand why each one was chosen, and decide which to include.
This approach is known as a user feedback loop, and it’s something we actively encourage with all our clients. It keeps humans in control, builds trust in the AI, and allows teams to streamline their workflow without giving up decision-making power, which is key for successful AI adoption.
Your internal linking agent will:
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Receive a blog brief or title
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Reference a spreadsheet containing existing blog posts
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Use keywords or tags to find related posts
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Suggest 2–5 internal links to include in the new article
Reminder: This guide is for you if you’ve built or are building a content-generating AI agent that creates articles for your business on n8n.
Tools you’ll need
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n8n (self-hosted or cloud)
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Google Sheet
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Optional: OpenAI or Claude (if connecting to a content agent)
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Basic n8n familiarity (eg: ability to add credential to access a Google sheet in n8n)
Step-by-Step: Building the Minimal RAG Workflow in n8n
1. Starting with the data
Whether you’re setting up a minimal RAG system or going full scale, your data is the foundation. And don’t worry — if you don’t have this in place yet, I’ll walk you through exactly what you need.
The first step is to create a structured source of truth for your internal links. In this example, we’re using a Google Sheet — lightweight, accessible, and perfect for MVP setups.
To help your agent understand and use the internal links effectively, your spreadsheet should include the following columns:
| Column Name | Description |
|---|---|
| Title | The title of the page or blog post |
| URL | The unique identifier — the full link to the page |
| Page Description | A clear summary of what the page is about; critical for link relevance |
| Tags | 2–5 keywords or themes describing the content |
| SEO Description (optional) | The meta description used for SEO — adds context for the model |
🧠 Remember: your AI is only as useful as the context you give it. If the description or tagging is vague, the agent will struggle to make helpful suggestions.
Now that you have your data source, we can integrate it to yout n8n agent. You’ll be surprised how simple — and powerful — this setup really is.
In the example I will use here, we’re using on of our client’s blog brief generator agent.
Here’s how it works: the agent pulls a topic from a Google Sheet the marketing team regularly updates with article ideas. Each entry includes:
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A working blog title
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Context about what they want to cover
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The intended purpose of the post (e.g. promotional, educational)
As an output, the agent generates a detailed blog brief that includes:
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Suggested target audience
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Key headlines
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SEO elements like target keywords and a draft meta description
This one agent alone has already saved their marketing team a huge amount of time.
Minimal RAG Configuration
To increase its value, we simply added the internal link spreadsheet as a tool to the OpenAI node.
- Step 1: In your OpenAI node, go to the Tools section.
- Step 2: Search for and select Google Sheet as your tool.
- Step 3: Configure the Google Sheet by linking it to the Internal Link Inventory spreadsheet you prepared earlier.
- Step 4: Rename the node clearly to reflect its purpose. This will help you stay organised as your workflow grows.
Once you’ve connected the spreadsheet, it’s important to tell the model how and when to use it in your prompt. This step is critical. You can see in the picture below an example of how we configure our prompt.
💡 Best practice:
We recommend creating a TOOLS section in your prompt, where you give specific instructions on how to use each tool (especially useful as your agent starts accessing multiple tools).
And that’s it! No heavy lifting. Just a spreadsheet, a clear prompt, and suddenly your AI is a lot more useful.
This is how the Agent looks like now after we added the minimal RAG to enable internal linking.
3. A few extra things to think about
🔁 Test, learn, iterate
It usually takes a few iterations to get the integration just right. You might need to adjust how you format the internal link data, or how you reference it in the prompt.
You’ll also start noticing areas where your data quality can improve — maybe the descriptions are too vague, or some tags are missing. Every small refinement boosts the agent’s performance.
We always recommend starting simple and iterating quickly until you reach the output quality you’re aiming for.
🧹 Data governance matters
Setting up your file is just the beginning. As your content library grows, you need to keep your spreadsheet updated — and keep data quality high.
In our client’s multi-agent system, agents help update the repository once a post is validated and published. A member of the marketing team also reviews data quality regularly. That’s what lightweight data governance looks like: simple routines that ensure your AI systems stay effective and relevant over time.
Next Step: Scaling with Supabase and embeddings
You can use this spreadsheet for a little while to prove value. But as your content volume grows, you’ll want to scale this setup. In the next article, we’ll explore how to:
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Move your data into a Supabase database
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Add full semantic search using embeddings
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Build a robust, scalable RAG architecture without overwhelming your team
Last words
Well done! If you followed this little tutorial, you’ve just built your first Minimal RAG setup using n8n, no fancy AI infrastructure required.
This system:
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Adds internal linking automation to your content pipeline
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Improves SEO and user experience
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Helps showcase AI value with a fast, low-risk MVP
Sometimes the best way to start with AI is not with big models — but with small, smart workflows.
Want help getting started?
If you’re exploring AI tools for marketing, this isn’t just about automation, it’s about scaling content profitably with AI.
We help organisations build AI strategies and launch useful agentic systems, fast. If you’re looking to make AI work for your marketing or product team, let’s talk.
