After building **12+ AI workflows** in n8n over the past few months, I wish I
had known these three things on day 1. Below is a guide that pulls together
three open-source tools I created (all MIT-licensed) so you can get reliable
results without blowing your budget.
## 1. Your prompts matter more than your model
Most people reach for the newest, most expensive model (e.g. `gpt-4o`) and
assume better output automatically follows. In practice a well-crafted prompt
for `gpt-4o-mini` often matches the quality of the larger model at a fraction
of the cost.
**Concrete prompt tips**
| Task type | Recommended temperature | Prompt pattern |
|-----------|------------------------|----------------|
| Text generation | `0.7` | "Write a 150-word LinkedIn post about {topic}.
Return JSON." |
| Classification | `0.1` | "Classify the sentiment. Output positive, neutral
or negative as JSON." |
| Extraction | `0.2` | "Extract all dates and amounts. Return an array of
{date, amount}." |
*Few-shot examples* (two short examples before the actual input) improve
consistency, especially for extraction. Also enable **JSON mode** to avoid
post-processing headaches.
All 20 ready-to-use prompts are collected in the **n8n Prompt Library** (6
categories) — clone it and drop a prompt directly into your workflow:
## 2. Know your costs BEFORE you build
Running AI calls inside n8n is cheap, but costs add up if you don’t monitor
them. I built a quick **n8n AI Cost Calculator** — it supports 10 models and
provides presets for common workflows:
**Real-world example** — repurposing a blog post to four platforms:
Model: gpt-4o-mini
Input tokens: ~500 per platform
Output tokens: ~300 per platform
AI nodes: 4 (one per platform)
Cost per run: ~$0.006
20 runs/day: $3.60/month
Most workflows stay well under $0.01 per execution with gpt-4o-mini.
## 3. Always add a human gate
AI can hallucinate, especially at higher temperatures. The simplest
human-in-the-loop pattern is a Telegram approval step:
1. **Generate** content in an OpenAI node
2. **Send** the output to Telegram with Approve/Reject buttons
3. **Wait** for the user to tap a button (n8n Wait node)
4. **Branch**: approve → publish, reject → log and re-run
This adds 10 seconds of human review but prevents disasters. Full workflow
## Quick reference
| Task | Temperature | Best model | Cost/run |
|------|-------------|------------|----------|
| Content generation | 0.7 | gpt-4o-mini | ~$0.003 |
| Classification | 0.1 | gpt-4o-mini | ~$0.001 |
| Data extraction | 0.2 | gpt-4o-mini | ~$0.005 |
| Summarization | 0.3 | gpt-4o-mini | ~$0.002 |
All three tools are open source and MIT-licensed. What’s the most surprising
cost you’ve seen in an n8n AI workflow, or which prompt pattern has saved you
the most time?
%, I strongly Agree…