How It Works

Where JSON TO TOON fits in your stack

JSON TO TOON sits between your existing data sources and your LLM prompts. You keep producing JSON the way you do today, then convert it to TOON only for the parts that go into prompts. This keeps your databases, APIs, and logs unchanged while making your LLM usage more efficient.

• Upstream: your app, API, or data pipeline still emits structured JSON for events, analytics, or user data.
• Middle: before calling an LLM, you convert that JSON to TOON (using the patterns you tested in the converter) and include TOON in the prompt instead of raw JSON.
• Downstream: the LLM reads TOON just like any other text, but you pay for fewer tokens and can often fit more context into each request.

All of this can be done without sending your real data to external services: the homepage converter runs fully in your browser, and production conversions can run inside your own backend or internal tools.

Real example: from JSON to TOON in a prompt

This example shows how a small JSON analytics payload turns into TOON and how you might include it in an LLM prompt. The structure stays the same—the only change is how compact the representation is.

{
  "sessions": [
    {"userId": 1, "page": "pricing", "duration": 42},
    {"userId": 1, "page": "docs", "duration": 18},
    {"userId": 2, "page": "home", "duration": 33}
  ]
}

sessions[3]{userId,page,duration}:
  1,pricing,42
  1,docs,18
  2,home,33

You are a product analyst. Summarize key insights from the following session data.

sessions[3]{userId,page,duration}:
  1,pricing,42
  1,docs,18
  2,home,33

Implementation checklist (5 quick steps)

When you are ready to move from experimenting in the converter to using TOON in your own product, this simple checklist can help you roll it out safely.

1. Identify high‑impact prompts. Start with a few prompts that send large, repetitive JSON payloads to GPT‑4, Claude, or Gemini and that you call frequently.
2. Design and test TOON formats. Paste those JSON payloads into the homepage converter, review the TOON output and token counts, and agree on a stable column order and naming convention.
3. Add conversion in code. In your backend or prompt‑building layer, convert JSON to the agreed TOON format and swap the JSON block in your prompts for the TOON block.
4. Monitor behavior and savings. Compare token usage, latency, and response quality before and after TOON. Keep the old JSON path available as a temporary fallback.
5. Standardize and document. Once TOON works well, document the pattern in your internal docs or prompt libraries so other features and teams can reuse the same token‑efficient structure.