Create reliable AI answers in a clean data format. It turns a natural language request into a structured result that follows a clear schema. Ideal for teams that need consistent fields for reports, apps, and dashboards.

You start it by clicking run. A Set node builds the prompt. An OpenAI chat model writes the first answer, and a basic chain passes it through a structured output parser that checks exact fields like state and a list of cities with names and population. If the answer does not fit the schema, an auto fixing parser calls a second OpenAI chat model to rewrite the result so it matches. The chain uses the validated parser so the final output returns as data, not free text. This raises data quality and cuts cleanup time.

Only an OpenAI key is needed. Swap the prompt and schema to fit products, store locations, support cases, or any list you manage. Teams often save 20 to 30 minutes per request and see fewer errors, which means faster publishing and smoother data flows.

• Manual trigger lets you run controlled tests on demand
• Set node stores and edits the prompt in one place
• Two OpenAI chat model nodes, one to create content and one to help fix invalid results
• Structured Output Parser defines the exact schema for state and city details
• Auto fixing Output Parser tries to repair outputs that do not match the schema
• Basic LLM Chain connects the prompt, the model, and the parser so data flows in order
• Sticky notes in the canvas explain each part for faster onboarding

• Reduce manual reformatting from 30 minutes to 2 minutes per data set
• Improve valid structured outputs to 98 percent with automatic checks and fixes
• Handle three times more requests without extra staff
• Keep field names and types consistent across teams
• Cut follow up edits between analysts and writers

1. Import the template into n8n: Create a new workflow in n8n > Click the three dots menu > Select 'Import from File' > Choose the downloaded JSON file.
2. You'll need accounts with OpenAI. See the Tools Required section above for links to create accounts with these services.
3. In the n8n credentials manager, create an OpenAI credential. Get your API key from the OpenAI account page, then paste it into the API Key field and save.
4. Open each OpenAI Chat Model node and choose your OpenAI credential from the Credential to connect with dropdown. Save the node.
5. Open the Structured Output Parser node and review the schema. Confirm fields like state and cities match the data you want. Edit names and types if needed, then save.
6. Open the Set node named Prompt and update the chatInput field with the request you want the model to answer. Keep the wording clear so fields can be filled.
7. Open the Basic LLM Chain node and confirm it shows an output parser and a language model connected. Leave defaults unless you need different behavior.
8. Click Execute Workflow to run a test. Check the final output. You should see structured fields that match the schema.
9. If the output fails validation, the auto fixing parser will attempt a repair. Review the fixed result to confirm the structure is correct.
10. Troubleshoot common issues: if you see an auth error, recheck the OpenAI API key. If you get schema errors, adjust the schema or prompt so all required fields are present and typed correctly.
11. When satisfied, duplicate the workflow and replace the prompt and schema for your real use case, such as product lists or support summaries.