I’m encountering an issue where the AI model (likely using OpenAI or another LLM) returns different responses for the same input and prompt in my n8n workflow. This is causing inconsistencies in automation, making it unreliable for production use.
Issue Details:
- When sending the same input text with the same prompt, I get different responses each time.
- This happens in both test mode and production executions.
- I’ve tried changing the temperature and setting it to 0 (zero), but the problem persists.
- The inconsistency affects automated decision-making and workflow reliability.
# Role:
You are an AI assistant specialized in extracting structured data from unstructured text. Your task is to process details about a trucking load and return the extracted information in a valid JSON format.
# Task:
You will receive load details in plain text. Your job is to extract and return the relevant information strictly in JSON format, matching the expected schema.
# Important Notes:
- Return only a valid JSON object matching the expected schema. Do not include any extra text, explanations, or code blocks.
- Ensure "offered_rate" is a number (not a string).
- If a detail is missing, return empty string instead of omitting the key.
- Do not add markdown, code formatting, or additional text —only return raw JSON.
- Use consistent formats:
- `"pickup_date"`: Full weekday and date format (e.g., `"Monday, February 17th"`).
- `"weight"`: Numerical value followed by `"lbs"` (e.g., `"30,000 lbs"`).
- `"offered_rate"`: Numeric (e.g., `3333` not `"3333"`).
Model output doesn’t fit required format
To continue the execution when this happens, change the ‘On Error’ parameter in the root node’s settings
Please share your workflow
Setup Details:
n8n Version: 1.73.0
Deployment: Google Cloud (Docker)
AI Model Used: (Specify the model, e.g., GPT-4o, Gemini 2.o flash)
Database: PostgreSQL
Execution Mode: queue
Hey @yow1da I would say that is kind of the nature of LLMs now. You cannot guarantee reliable output. That being said, you can add something like Auto-fixing Output Parser node documentation | n8n Docs to auto fix broken output or even add another LLM call to the chain and have it repair / review the JSON. I would probably opt for testing both on multiple examples (think 50+) and see which one works more consistently. I think there’s nothing wrong with your prompt in general. Just add a double layer of security.
Looking at the retry prompt, it’s essentially giving it for another round of correction to the LLM. If you want to be extra sure, you could still add an error workflow for failed executions but configure the AI Agent to still run through on error (not sure if your workflow is processing multiple items at once)
You changed the prompt in the Auto-fixing Output Parser - that’s not recommended because they use certain variable in there that give you the functionality you’re looking for
As soon as I am switching from AI Agent to Basic LLM Chain, I don’t have any problems any more. Could that be an option in your setup?
If in your case you can replace the ai agent with another simple node (‘Basic LLM Chain’, ‘Infomation Extractor’), this will help you solve the problem
