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Pramod Kumar
Pramod Kumar

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Why JSON Breaks in AI Pipelines — and the TOON Format I Built to Fix It

Why JSON Breaks in AI Pipelines — and the TOON Format I Built to Fix It

JSON has been the default data format for decades. It’s simple, readable, and works almost everywhere.

But recently, while building AI-driven workflows, I kept running into the same problem:

👉 JSON isn’t designed for meaning. It’s designed for structure.


The Problem I Hit

When working with LLMs and agent-based systems, I noticed:

  • JSON is too verbose for iterative reasoning
  • Nested structures become hard to interpret semantically
  • Relationships between data are implicit, not explicit
  • Small changes break parsing or require rigid schemas

Example

{
  "task": {
    "name": "generateReport",
    "input": {
      "user": "Alex",
      "role": "developer"
    }
  }
}
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This works for machines — but for AI systems trying to reason, it’s noisy.


What I Needed Instead

I wasn’t looking for a “better JSON”.

I needed something that:

  • Is human-readable at a glance
  • Represents relationships clearly
  • Works well with AI parsing + generation
  • Reduces unnecessary syntax

Introducing TOON (Experimental)

So I started experimenting with a lightweight format I’m calling TOON.

Same data in TOON:

task:
  name -> generateReport
  input:
    user -> Alex
    role -> developer
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What’s Different?

1. Less syntax, more signal

No quotes, fewer brackets — easier to scan.

2. Explicit relationships

Using -> makes intent clearer than key-value ambiguity.

3. AI-friendly structure

LLMs tend to:

  • generate this format more consistently
  • interpret it with fewer errors

Where This Actually Helped

In my tests (early stage):

  • Prompt-to-structure conversion became more stable
  • Parsing required less strict validation
  • Debugging outputs was faster

But It’s Not Perfect

This is not a “JSON killer”.

TOON currently lacks:

  • Standardization
  • Tooling ecosystem
  • Performance benchmarks at scale

And JSON is still the best choice for:

  • APIs
  • Production systems
  • Interoperability

So Why Explore This?

Because AI systems are changing how we think about data.

We’re moving from:

“How do we structure data for machines?”

to:

“How do we structure data for reasoning systems?”

Full Article Medium Link: https://medium.com/stackademic/farewell-json-hello-toon-the-future-just-arrived-561b74a0059e

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Pramod Kumar

“How do we structure data for machines?”