In this categoryAutomation · 38
- How to Self-Host n8n with Docker for AI WorkflowsStart
- How to Extract Structured Data from PDFs with n8n
- How to Add OpenAI Credentials to n8n
- How to Connect Anthropic Claude as the Model in n8n Workflows
- How to Build an AI Messaging / Chatbot Automation (No Code)
- How to Build an AI Agent with Tools in n8n
- How to Build an n8n Workflow That Summarizes New Emails with AI
- How to Build a RAG Chatbot Over Your Docs in n8n
- How to Auto-Classify and Route Support Tickets with AI in n8n
- How to Schedule Daily AI Content Generation in n8n
- How to Extract Structured Data from PDFs with AI in n8n
- How to Add Error Handling and Retries to n8n AI Workflows
- How to Transcribe and Summarize Audio with AI in n8n
- How to Summarize Incoming Emails with AI in Make.com
- How to Auto-Classify Support Tickets with AI in Make.com
- How to Turn RSS Headlines into AI Blog Drafts in Make.com
- How to Build an AI Telegram Chatbot in Make.com
- How to Answer Questions from Your Docs with AI in Make.com
- How to Auto-Transcribe Audio Files with Whisper in Make.com
- How to Extract Invoice Data from PDFs with AI Vision in Make.com
- How to Generate Images from a Spreadsheet with AI in Make.com
- How to Run AI Sentiment Analysis on New Reviews in Make.com
- How to Auto-Translate Content into Multiple Languages in Make.com
- How to Handle AI Errors and Rate Limits in Make.com Scenarios
- How to Build Your First Zap with an AI Step
- How to Use Your Own OpenAI API Key in Zapier
- How to Auto-Summarize Form Submissions and Post Them to Slack
- How to Auto-Generate Social Media Captions From New Blog Posts
- How to Extract Structured Data From Emails Using a Zapier AI Step
- How to Call the Claude API From Zapier Using Webhooks
- How to Build a Simple AI Chatbot with Zapier Interfaces and Tables
- How to Build a Multi-Step AI Research Agent in Zapier
- How to Auto-Categorize and Route Support Tickets with AI and Paths
- How to Cut AI Task Usage in Zapier With Filters and Formatter
- How to Debug AI Steps in Zapier Using Zap History
How to Debug AI Steps in Zapier Using Zap History
Find out why an AI step returned the wrong answer or broke a later action by reading the run logs and reproducing it.
AI steps fail in their own ways: empty output, extra commentary that breaks a JSON parser, or a label that no longer matches a Paths rule. Zapier's Zap History shows the exact input and output of every run, which is almost always enough to find the cause. This guide is your debugging routine.
What you need
- A live Zap with an AI step that is misbehaving
- Access to Zap History (in the left sidebar)
- About 10 minutes
Step 1: Open the failed run
Go to Zap History and find the run that went wrong. Click it to expand every step. Each step shows Data In and Data Out, so you can trace the exact values that flowed through the Zap on that run.
Step 2: Inspect the AI Data Out
Open the AI step and read its Data Out exactly as returned. The usual culprit is the model wrapping JSON in prose or a code fence, which a parser then rejects. Seeing the raw text tells you precisely what to fix.
Sure! Here is the JSON you asked for:
```json
{ "name": "Dana", "total": 148.5 }
```Step 3: Fix the prompt and re-test
Edit the AI step prompt to forbid extra text explicitly, and lower the temperature if you control it. Then use the editor's Test button to run the step again with the same sample and confirm the output is now clean.
Reply with raw JSON only. No prose. No code fences.
No leading or trailing characters. Start with { and
end with }.
{ "name": string, "total": number }Step 4: Replay the run
Back in Zap History you can often use Replay on the failed run to re-send the same data through the fixed Zap. This confirms the fix works on the exact case that broke, without waiting for a new live event.
Result: You can pinpoint why any AI step misbehaved by reading its real input and output, fix the prompt, replay the exact failure, and confirm the Zap now runs clean end to end.
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