DEV Community

Cover image for JSON vs TOON for AI Prompts: Why Developers Should Care (Now)
Ritesh Kumar Sinha
Ritesh Kumar Sinha

Posted on

JSON vs TOON for AI Prompts: Why Developers Should Care (Now)

#ai #llm #promptengineering #developers

If you’re deep into GenAI, agent orchestration, or building LLM-powered dev workflows, you’re probably shipping tons of structured data between models, agents, and microservices. You know the drill: JSON everywhere. But what if there’s something better for the new “token is money, structure is king” era? That’s where TOON steps up.

What’s Wrong with JSON (…for AI, anyway)?

Don’t get me wrong—JSON still rules dev APIs. But as soon as you pipe structured data into LLMs and multi-agent prompts, a few problems bite hard:

  • Tokens add up. Every bracket, field, and value costs money (literally, if you’re on OpenAI token pricing).
  • Lossy for structure. JSON is readable for us, but LLMs sometimes guess at intent or lose field relations—especially in big, nested blobs.
  • Zero schema signals. Model-to-model or agent-to-agent, everything is just list/field soup unless you add custom validation.

Why TOON? What’s Special?

TOON is a newer, model-first data format for AI and agent pipelines. It’s not a full JSON replacement—but for agent workflows, it’s a level up. Here’s why:

1. Token- & Model-Efficient by Design

TOON compresses arrays and repeated objects in a tabular layout that slashes tokens and draws the LLM’s “eye” to structure. Quick compare:

JSON :


[
    {"sku": "P123", "qty": 2, "price": 100},
    {"sku": "P124", "qty": 1, "price": 150}
]
Enter fullscreen mode Exit fullscreen mode

TOON :


items {sku, qty, price}:
P123, 2, 100
P124, 1, 150
Enter fullscreen mode Exit fullscreen mode
  • Fewer tokens = lower cost + more context per prompt.
  • Explicit structure = less LLM hallucination.

2. Schema Signals: Model- and Agent-Friendly

TOON lets you declare shapes up front—array lengths (items[3]), known field-sets ({sku,qty,price})—so both agents and models can “expect” the right data. If you’re orchestrating multi-agent flows, this wins big:

  • No guessing array length = easier downstream validation.
  • Cleaner contracts = agent workflows catch drift early.

3. When to Use TOON vs JSON vs CSV?

TOON shines when:

  • You have medium-complex structures (arrays of similar objects, but not crazy-deep nesting).
  • You pass data between models/agents inside prompts—token budget matters!
  • You want “schema hints” without formal schemas.

Stick to JSON for deeply nested, highly irregular data.

Use CSV for super-flat, untyped tabular dumps.

Scenario Use
Model-to-model pipe (structured list) TOON
Logging & retrieval-augmented prompts TOON
Classic web APIs, deep nesting JSON
Flat tabular export (no types) CSV

Real-World Usage (How I Use It)

  • Prompting with agent frameworks: I format reference data (like tool specs or task lists) as TOON blocks—models “see” tabular intent, and I get 20–30% token savings.
  • Microservice contracts in AI stacks: Instead of vague JSON, explicit TOON contracts help when my AI microservices need strict data exchanges but don’t want to manage giant JSON schemas.
  • Debugging agent drift: If my pipeline borks, it’s easier to spot mismatches in TOON’s schema-centric view.

Before You Rewrite Everything…

  • Analyze your AI/data flows: Where do tokens pinch? Where do agents or models misinterpret structure?
  • Test TOON formatting on your biggest/most crucial prompts.
  • Don’t blindly swap for legacy REST APIs—TOON isn’t for everything.

Conclusion

TOON isn’t just some niche data hack—it’s a timely response to real pain points in generative AI and agentic development. As we push the boundaries with larger context windows, more complex agent interactions, and tighter budgets for tokens and compute, the way we structure prompt data matters more than ever.

JSON got us here, powering APIs, frontends, and integrations everywhere.

TOON helps us get where we’re going next:

  • Smarter data flows between models and agents
  • Clearer, enforceable contracts
  • Lean, efficient prompts—without sacrificing structure or validation

If you’re serious about building next-gen AI, LLM, or multi-agent systems, TOON deserves your attention. It’s not about ditching JSON overnight—it’s about picking the right tool for the job, especially when every token and every schema contract counts.

If you liked this:

  • Try formatting your next agent-to-agent payload in TOON—spot the token drop.
  • Share your own TOON/JSON quirks in the comments.

Top comments (0)