Format Module for Output Structure

Learning Objectives

• Understand how the Format Module controls your AI output structure
• Learn to specify output formats like Markdown, JSON, Notion, and Airtable
• Apply format settings to ensure your AI outputs match your project needs

Introduction

When you're working with AI-generated content, getting the right output format can save hours of manual formatting. The Format Module in Modular Prompting handles this for you. It transforms messy AI responses into clean, structured outputs that slot straight into your workflow.

This chapter shows you how to configure output formats, avoid common formatting mistakes, and set up templates you can reuse across projects.

Lessons

Understanding the Format Module

The Format Module acts as your output controller. Instead of receiving unstructured AI responses, you define exactly how the content should appear and be organised.

Think of it as setting up a template. You tell the AI: "Give me a blog post in Markdown format with specific headings" or "Create a data export formatted for Airtable with these exact columns."

Here's what the Format Module handles for you:

• Content structure and hierarchy
• Data formatting and layout
• Platform-specific requirements
• Consistent output styling

Setting Up Basic Output Formats

Start with these common format types to get familiar with the system.

For Markdown outputs:

• Specify heading levels you need
• Define how lists should appear
• Set up any special formatting requirements

For JSON data:

• Map out your data structure
• Define required fields and data types
• Set up proper nesting if needed

For platform exports (Notion, Airtable):

• Match the platform's field requirements
• Define column types and restrictions
• Set up any required metadata

The key is being specific about what you need. Vague instructions lead to outputs that need manual fixing.

Creating Reusable Format Templates

Once you've got a format working well, save it as a template. This stops you rebuilding the same structure every time.

Name your templates clearly. "Blog post – Markdown" is better than "Template 1". You'll thank yourself later when you're scrolling through a long list.

Test your templates with different content types. A format that works for product descriptions might break with technical tutorials.

Keep a few go-to templates ready:

• Standard blog post format
• Data export format for your CRM
• Social media content structure
• Report template with consistent sections

Troubleshooting Format Issues

When outputs don't match what you expected, check these common problems first.

Missing or incorrect headings: Your heading hierarchy might be unclear. Specify exactly which heading levels to use and when.

Data in wrong format: Double-check your field definitions match what the target platform expects.

Inconsistent styling: Make sure your template covers all the content types you're generating.

Platform compatibility issues: Different platforms have different requirements. Test with small samples before running large batches.

Most format problems come from being too general in your instructions. The more specific you are, the better your results.

Practice

Set up a basic Markdown format for a blog post structure. Include:

• One main heading
• Three subheadings
• Bullet points under each section
• A conclusion paragraph

Test it with a simple topic like "Benefits of morning exercise" to see how the structure works.

FAQs

Where do I configure format settings?
Access the Format Module through your Modular Prompting dashboard. Look for the output configuration section where you can define structure requirements.

Can I modify formats after creating them?
Yes, you can edit and update format templates. Just remember to test changes with sample content before applying them to important projects.

Do different AI models handle formats differently?
Some models follow format instructions more precisely than others. If you're getting inconsistent results, try being more explicit in your format requirements.

How specific should I be with format instructions?
Be as specific as your project needs. For simple content, basic structure is fine. For complex data or strict platform requirements, define every detail.

Jargon Buster

Format Module – The component that controls how AI outputs are structured and presented

Output Structure – The organisation, formatting, and layout of AI-generated content

Template – A saved format configuration you can reuse across different projects

Field Mapping – Defining how data should be organised into specific columns or categories

Wrap-up

The Format Module removes the tedious work of reformatting AI outputs. By setting clear structure requirements upfront, you get usable content that fits straight into your workflow.

Start with simple formats and build complexity as you get comfortable. Focus on creating a few solid templates that cover your most common use cases.

Next, you'll learn about combining the Format Module with other prompt components to create more sophisticated AI workflows.

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