How to Build an AI Code Reviewer That Teaches Instead of Fixing

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How to Build an AI Code Reviewer That Teaches Instead of Fixing

Most AI code tools want to autocomplete your student’s homework. That’s the opposite of teaching. I built a workflow that takes a GitHub repository, runs it through an LLM with structured evaluation prompts, and returns scored feedback on naming, structure, logic, and completeness — with line-by-line suggestions that ask questions instead of handing out answers. The whole thing runs on N8N, costs almost nothing, and takes about an afternoon to set up.

If you teach programming, or review code from junior devs, and you’re tired of writing the same “rename this variable” comment for the 400th time, this is for you.

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Init

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What You Need

Before diving in, ensure you have:

• access to an N8N account

• a well-crafted N8N workflow

• JSON/Markdown renderer system (GitHub repository is an ok solution)

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N8N

N8N is a workflow automation platform — think Zapier, but with actual code nodes and a visual editor that doesn’t make you want to close your laptop. You chain together triggers, logic blocks, HTTP calls, and AI model integrations into a single executable pipeline.

What makes it particularly useful here is the bridge between structured code logic and LLM interaction. You can write JavaScript to sanitize data, call the GitHub API, build prompts dynamically, then hand everything to an AI node — all inside the same workflow. The visual canvas also makes it easy to debug: you click a node and see exactly what data passed through it.

The framework supports code in JS as well as Python, but if you don’t need a special Python library, JS is definitely easier to install. It can do everything we need, so this guide will be focused on JS only.

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The Cheapest Way

N8N’s cloud starter tier sits around $20/month, which gets you up to 2,500 workflow executions. For a teacher grading student repos, that's more than enough — even if you're running it multiple times per student per assignment.$

But if you want to self-host and skip the subscription entirely, you can run N8N on your own server or on simple localhost for free. The only cost becomes the LLM tokens themselves. I’m building ZipOps partly for setups like this — lightweight self-hosted tooling without the cloud bill.

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JSON/Markdown Renderer

The workflow outputs structured data — either JSON or Markdown — so you need something to render it into a readable format. There are a few practical options depending on your setup, and I’ll show examples of each later in this article:

• HTML/JS web page rendering JSON directly. This is the most flexible approach — you build a small page that reads the JSON and displays scores, suggestions, and file-level feedback in a proper UI. Standard web stack, full control over layout.

• Obsidian rendering Markdown. If you already use Obsidian for notes, you can paste the Markdown output directly into a vault and get a clean, navigable review document. Share it through a GitHub repo and students can read it anywhere.

• Discord rendering Markdown. Copy-paste the output into a Discord channel and it renders natively. Useful for quick feedback loops — especially if your class already has a server (most of your students probably use Discord, anyway).

Each approach has tradeoffs. JSON gives you programmatic control but needs a renderer. Markdown is instantly readable but harder to build dynamic interfaces around.

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N8N Workflow Framework

The full workflow follows three stages. First, you define how the process starts — either a manual trigger for testing or a webhook for production use. Second, you define the actual analysis pipeline: fetch the repo, identify source files, build prompts, run the evaluation. Third, you decide how to package the output — JSON for structured rendering, Markdown for quick sharing.