Foundation

How to Get Structured Output With JSON and Templates From AI

Getting structured output with JSON and templates from AI means defining the schema in your prompt and getting data back that your application or spreadsheet can parse directly. You provide the structure, AI fills in the values. For example: extract the following fields from this email and return JSON: sender, subject, action_required (yes/no), deadline. AI returns a JSON object you can parse without reformatting. At aidowith.me, the Context Engineering route covers 8 steps for controlling AI context and output. The JSON and template step includes schema definition patterns, nested object handling, and how to validate AI output before using it in a downstream workflow. The route takes about 1 hour. Users connect this output directly to Make, Zapier, or Google Sheets to build pipelines that process text and extract structured data without a single manual step.

8 steps ~1h For all professionals Free
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The Problem and the Fix

Without a skill

  • You need to extract data from unstructured text and paste it into a database, but AI output requires manual cleanup.
  • AI output format is inconsistent across runs, which breaks any downstream automation you've built.
  • You know JSON exists but don't know how to reliably get AI to return it in the format your app expects.

With aidowith.me

  • Define the JSON schema in the prompt and get consistently structured output across every run.
  • Use output templates with field names and value types to eliminate formatting inconsistency.
  • Connect AI output directly to Make, Zapier, or a spreadsheet without a manual reformatting step.

Who Builds This With AI

Marketers

Content, campaigns, and briefs done in hours instead of days.

Sales & BizDev

Prep calls, draft outreach, research prospects in minutes.

Managers & Leads

Reports, presentations, and team comms handled faster.

How It Works

1

Define your JSON schema

Write out the fields, types, and nesting structure your downstream system expects.

2

Embed the schema in the prompt

Paste the schema into the prompt and tell AI to return a JSON object matching it.

3

Validate and connect

Run a test, check the output matches the schema, and connect it to your workflow.

Build Reliable AI Data Pipelines With JSON Templates

The 8-step Context Engineering route covers JSON output and 7 other patterns for consistent AI output in about 1 hour.

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What You Walk Away With

Define your JSON schema

Embed the schema in the prompt

Validate and connect

Connect AI output directly to Make, Zapier, or a spreadsheet without a manual reformatting step.

"I went from manually copying data from AI to having a Make scenario that parses AI JSON output automatically. The context engineering route made that click."
- Automation specialist, operations team
See full skill: Context Engineering

Questions

Include the JSON schema in your prompt. Write: Return the result as JSON with this structure: name as string, priority as high/medium/low, due_date in YYYY-MM-DD format. Most frontier AI models follow explicit schemas reliably. For complex objects, paste a complete example and tell AI to match the format. Test with a sample input before connecting to any automation.

An output template defines the structure before AI fills in the content. Example: a meeting summary template with fields for date, attendees, decisions, and action items. Paste the template into the prompt with empty or placeholder values and ask AI to fill it in. The aidowith.me Context Engineering route covers this pattern with 5 template types across common business workflows.

Context engineering is the practice of designing what goes into an AI prompt, including instructions, examples, constraints, and output schemas, so the response is usable without editing. For structured output, it means writing prompts that specify format, field names, and data types upfront. The route at aidowith.me covers 8 context engineering patterns that improve output quality and consistency.