You must have faced these ChatGPT issues in your life.
These all happen because of limited context windows in Large Language Models (LLMs). Every bracket, space, and key-value pair in JSON consumes valuable tokens. When data grows — models choke.
To fix this, we need a way to handle structured data in an LLM-optimized format — that’s where TOON comes in.
TOON (Token-Oriented Object Notation) is a lightweight data representation format designed for LLMs like ChatGPT, Gemini, or Claude. Unlike JSON or YAML, TOON reduces token usage by minimizing syntax redundancy while maintaining structure readability.
1
2
3
4
5
6
7
8
9
10
11
12
13
14
// JSON Format
{
"user": {
"name": "Zeeshan Ali",
"role": "Engineer",
"skills": ["AI", "LLMs", "Cloud"]
}
}
// TOON Format
user:
name: Zeeshan Ali
role: Engineer
skills: items[3]: "AI","LLMs","Cloud"
Notice how TOON removes unnecessary punctuation while keeping data structured and readable. Every saved symbol means fewer tokens — and fewer tokens mean more context room for reasoning and generation.
1
2
3
4
5
6
7
8
9
10
11
12
13
14
// JSON Example — Nested Configuration
{
"app": {
"theme": { "mode": "dark", "font": "Inter" },
"version": "1.4.2"
}
}
// TOON Equivalent
app:
theme:
mode: dark
font: Inter
version: 1.4.2
1
2
3
4
5
6
7
8
9
10
11
12
// JSON Example — Array of Objects
{
"employees": [
{ "name": "Ali", "department": "AI" },
{ "name": "Sara", "department": "Web" }
]
}
// TOON Equivalent
employees: items[2]{name, department}:
Ali, AI
Sara, Web
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
{
"company": {
"name": "P2PClouds",
"founded": 2018,
"departments": [
{
"name": "AI Research",
"lead": "Zeeshan Ali",
"employees": [
{ "name": "Sara", "role": "Data Scientist", "skills": ["Python", "TensorFlow", "LLMs"] },
{ "name": "Ahmed", "role": "ML Engineer", "skills": ["PyTorch", "NLP"] }
],
"projects": [
{ "title": "TOON Format", "status": "Active", "metrics": { "efficiency": "40% gain", "adoption": "Beta" } },
{ "title": "Toxicity Detection", "status": "Paused", "metrics": { "accuracy": "92%", "recall": "88%" } }
]
},
{
"name": "Web Development",
"lead": "Ayesha Khan",
"employees": [
{ "name": "Ali", "role": "Frontend Developer", "skills": ["React", "Next.js"] },
{ "name": "Bilal", "role": "Backend Developer", "skills": ["Node.js", "MongoDB"] }
]
}
],
"locations": ["Pakistan", "UAE", "USA"],
"products": {
"active": ["AI Agent", "PromptHub", "CloudSync"],
"upcoming": ["DataVerse", "SmartLLM"]
}
}
}
company:
name: P2PClouds
founded: 2018
departments: items[2]{name, lead, employees, projects}:
AI Research, Zeeshan Ali,
employees: items[2]{name, role, skills}:
Sara, Data Scientist, [Python, TensorFlow, LLMs]
Ahmed, ML Engineer, [PyTorch, NLP]
projects: items[2]{title, status, metrics}:
TOON Format, Active, metrics:{efficiency: 40% gain, adoption: Beta}
Toxicity Detection, Paused, metrics:{accuracy: 92%, recall: 88%}
Web Development, Ayesha Khan,
employees: items[2]{name, role, skills}:
Ali, Frontend Developer, [React, Next.js]
Bilal, Backend Developer, [Node.js, MongoDB]
locations: [Pakistan, UAE, USA]
products:
active: [AI Agent, PromptHub, CloudSync]
upcoming: [DataVerse, SmartLLM]
🚀 Benefits of TOON Format
Token-Efficient: Uses 30–40% fewer tokens compared to JSON, directly improving LLM throughput. Readable Yet Compact: Designed to be easily human-readable like YAML, but more LLM-friendly. Faster Processing: Less tokenization overhead means faster prompt processing and responses. Context Window Optimization: Ideal for multi-turn chat or long context applications. Cross-Compatible: Easily convertible from JSON or YAML for smooth integration.
🧠 Applications of TOON
Prompt Engineering – store and format system, user, and context data efficiently. Chatbot Memory Systems – manage multi-turn context without exceeding token limits. AI Agents – define roles, objectives, and tool schemas with minimal token burst. Configuration Files – similar to TOON/YAML, but optimized for token parsing in LLMs. Data Serialization in Edge AI – transmit structured data with reduced payload size.
🌍 Why TOON Matters in the LLM Era
As AI systems evolve, context efficiency becomes more critical than model size. TOON represents a shift toward data formats that think like LLMs — efficient, structured, and context-aware.
In short, TOON helps you talk to AI in its native language — concise, meaningful, and token-smart.
Authored by Zeeshan Ali — AI Engineer, Prompt Architect, and TOON Format Advocate.
