How we use n8n: Senior Dev Rel, Education

n8n ·2026-06-22 ·2 min read

Summary written by us from the video's transcript. The video, and everything in it, is n8n's work.

Learn how a senior developer relations professional uses n8n and AI automation to manage education partners, streamline curriculum assessments, and focus on high‑impact work.

Takeaways

Automation of partner outreach reduces daily email review time to 15‑30 minutes while keeping a human in the loop.
Curriculum assessment workflows cut processing time from six hours to ten minutes per submission with a 95 % approval rate.
Keeping workflows simple (<15 nodes) makes them easier to maintain and adapt when models change.
Regular prompt evaluation and model selection balance cost and performance, using cheaper models for most tasks.
Human oversight of AI outputs prevents costly errors and ensures reliable automation.

Role and Scope of the Education Team

Jamie is the Senior Dev Rel in Education at n8n, running a one‑person team that handles internal training, external training, course development, and partnerships with over 200 education partners. The goal is to teach users how to use n8n effectively, reducing frustration and increasing adoption.

Automating Partner Outreach

All initial outreach to education partners is fully automated using n8n workflows that generate draft emails. Jamie reviews the drafts for 15‑30 minutes a day, acting as the human in the loop before responding personally. This reduces email handling from several hours to a few minutes each day.

Curriculum Assessment Workflow

Jamie built an automated curriculum assessment tool where partners submit their curricula, the system checks against n8n’s standards, and produces a report. What once took six hours per curriculum now takes about ten minutes, with a 95 % pass‑rate after iterative prompt engineering. The workflows typically contain fewer than 15 nodes to stay simple and reliable.

Prompt Management and Model Selection

Workflows rely on AI prompts that are iterated locally before being deployed. Jamie runs evaluations whenever a new model version is released, choosing cheaper models (e.g., Sonnet for ~75 % of tasks) when they meet quality needs, and reserving larger models like Opus for more complex jobs. Open‑source models handle lightweight tasks such as email labeling.

Human‑in‑the‑Loop Safeguards

Jamie never lets AI act unchecked: every draft email and assessment report is manually reviewed. When a workflow fails—such as timing out on a 280‑page submission—she reruns it with the missing files or processes it locally. This oversight prevents the kind of catastrophic errors seen when users release AI without supervision.

Impact on Workload and Team Size

Automation has freed Jamie from 4‑6 full‑time equivalents, roughly 240 hours per week. She now works a balanced ~42‑hour week, spending over 60 % of her time on high‑impact activities like course creation and partner support rather than repetitive email handling.

Dogfooding n8n to Teach n8n

Using n8n to build the education workflows exemplifies ‘dogfooding’: teaching the product with the product itself. Jamie values the visual workflow interface over custom code, allowing quick debugging by inspecting execution logs and rerunning failed steps without deep log analysis.