When Non-Developers Need to Understand JSON — 5 Real Situations

Situation 1: E-commerce API integrations

If you run an online store on Shopify, WooCommerce, or a Korean platform like Cafe24 or Smartstore, and you have ever tried to connect it to an external tool — inventory management, delivery tracking, accounting software — you have dealt with the output of an API. That output is almost always JSON.

Here is what a product record from a typical e-commerce API looks like:

{
  "product_id": 482910,
  "name": "Cotton Tote Bag",
  "price": 19900,
  "stock": 34,
  "category": "bags",
  "active": true
}

You don't need to write this. But if a developer says "the API returns this structure," or you're mapping fields in a no-code tool, recognizing the key-value pairs helps you understand what you're looking at — and gives you enough vocabulary to describe what you actually need.

Situation 2: ChatGPT and AI automation

If you want to use the ChatGPT API, Claude API, or any AI service directly — rather than through a chatbot interface — you communicate with it using JSON. The request you send specifies the model, the message, and any parameters. The response comes back in JSON too.

Here is a simplified version of what an API call to an AI service looks like:

{
  "model": "gpt-4o",
  "messages": [
    {"role": "user", "content": "Summarize this in one sentence."}
  ],
  "temperature": 0.7
}

Tools like n8n, Zapier, and Make (formerly Integromat) let you build automations that call AI APIs without writing code. But you still need to read and sometimes adjust the JSON payload — knowing what "messages" and "content" refer to is enough to troubleshoot when something breaks.

Situation 3: Google Analytics and data exports

GA4, Google Ads, Meta Ads, and most analytics and advertising platforms let you export raw data. When you export to a format other than Excel or CSV, it often comes as JSON — especially when you're pulling data via their APIs for use in a dashboard or spreadsheet.

The structure usually looks like a list of records:

[
  {"date": "2026-04-01", "sessions": 1240, "users": 980},
  {"date": "2026-04-02", "sessions": 1380, "users": 1050}
]

If you work with a data analyst or use a tool like Looker Studio or Notion databases, you may be asked to provide data in JSON format, or to connect a data source that delivers it. Recognizing this structure means you can verify the data looks right without needing to understand every detail.

Situation 4: Zapier, n8n, and no-code automation

No-code automation tools are designed to hide JSON from you. But they don't always succeed. When a "Custom Webhook" step fails, or a field mapping doesn't work, you often end up looking at the raw payload — which is JSON.

Common moments where you encounter it in these tools:

• When you choose "Custom request body" for an HTTP action and need to specify the data to send
• When you look at the test result of a step and see the raw data structure
• When you map data from one step to another and the field is nested (e.g., data.user.email means the email key is inside a user object inside a data object)

Once you can read a JSON structure and trace how one field relates to another, debugging automations becomes much less frustrating.

Situation 5: Talking to developers

Even if you never touch JSON directly, understanding its structure makes communication with developers clearer. Instead of "I need the order data," you can say "I need the order ID, customer name, and total amount — in a format where each order is a separate object." That's a much more actionable request.

Similarly, if a developer shows you an API response and asks what fields you need, being able to point to specific keys in the JSON saves several back-and-forth exchanges.

How to read JSON when you're not sure what you're looking at

When JSON looks overwhelming, try this approach:

1. Paste it into a JSON formatter — it will indent and color-code it so the structure is easier to see. Our JSON formatter tool works in your browser without uploading anything.
2. Look for curly braces {} — these contain objects (individual records)
3. Look for square brackets [] — these contain lists (multiple records)
4. Find the key that matches what you're looking for and read the value next to it

That's usually enough to find what you need without needing to understand the entire structure.