TOON format excels in real-world applications where token efficiency directly impacts cost and performance. This guide showcases practical examples across different industries and use cases, demonstrating how TOON optimizes data for LLM applications.
Each example includes context, the TOON implementation, and explanation of token savings. You can test these examples with our TOON Validator and convert your own data using JSON to TOON converter.
Example 1: Customer Churn Analysis
Use Case: AI-powered customer analytics platform analyzing churn risk for SaaS subscription business
TOON Format
# Customer churn analysis - Q4 2024
# Task: Identify high-risk customers and recommend retention strategies
customers[10]{id,name,company,mrr,tenure_months,support_tickets,nps_score,churn_risk}:
C001,Sarah Mitchell,Acme Corp,299,24,2,9,0.15
C002,Michael Chen,StartupXYZ,999,36,1,10,0.08
C003,Jennifer Kumar,TechVentures,99,6,8,4,0.82
C004,David Park,Innovation Labs,499,18,3,7,0.35
C005,Emma Wilson,Digital Solutions,99,3,12,3,0.89
C006,James Rodriguez,Enterprise Co,1499,48,0,9,0.05
C007,Lisa Thompson,Cloud Systems,299,15,5,6,0.48
C008,Robert Kim,DataFlow Inc,799,30,2,8,0.22
C009,Maria Garcia,AgileWorks,199,9,7,5,0.71
C010,Thomas Anderson,ScaleUp Ltd,599,21,4,7,0.38
analysis_parameters:
high_risk_threshold: 0.7
focus_metrics: "support_tickets, tenure_months, nps_score"
retention_budget: 5000
task: |
Analyze the customer data and:
1. Identify customers with churn_risk > 0.7
2. Determine common patterns among high-risk customers
3. Recommend specific retention strategies for top 3 at-risk customers
4. Estimate retention cost vs customer lifetime valueToken Efficiency:
- • JSON equivalent: ~420 tokens
- • TOON format: ~210 tokens
- • Savings: 50% (210 tokens saved)
- • Cost impact: $0.0021 saved per API call at GPT-4 pricing
Example 2: E-commerce Order Processing
Use Case: AI assistant helping customers track orders and suggest related products
TOON Format
# Customer order history for personalized recommendations customer: id: "U-45892" name: "Alex Johnson" email: "[email protected]" tier: "Gold" lifetime_value: 3247.50 recent_orders[5]{order_id,date,total,status,items_count}: ORD-1001,2025-01-10,149.99,delivered,3 ORD-1002,2025-01-05,89.50,delivered,2 ORD-1003,2024-12-28,234.75,delivered,5 ORD-1004,2024-12-15,67.99,delivered,1 ORD-1005,2024-12-01,445.00,delivered,4 current_cart[3]{product_id,name,quantity,price}: P-8472,Wireless Headphones,1,129.99 P-3391,USB-C Cable,2,15.99 P-7621,Phone Case,1,24.99 browsing_history[4]: "laptops", "monitors", "keyboards", "desk accessories" task: | Based on the customer's purchase history and current cart: 1. Recommend 3 complementary products 2. Suggest bundle deals to increase order value 3. Identify if customer qualifies for free shipping 4. Personalize the checkout message
Why TOON Works Here:
E-commerce systems frequently pass order history and cart data to LLMs for personalization. The structured array notation for orders and cart items reduces token count significantly, allowing more browsing history and customer context within the same token budget.
Example 3: Financial Transaction Analysis
Use Case: Fraud detection system analyzing transaction patterns for anomaly detection
TOON Format
# Real-time fraud detection analysis
account:
account_id: "ACC-789012"
holder: "Sarah Mitchell"
account_type: "checking"
current_balance: 12847.50
avg_monthly_spend: 3200.00
recent_transactions[15]{txn_id,date,merchant,amount,category,location,flagged}:
TX001,2025-01-15T14:23:00Z,Amazon,89.99,shopping,Seattle WA,false
TX002,2025-01-15T14:25:00Z,Gas Station,45.00,fuel,Seattle WA,false
TX003,2025-01-15T14:27:00Z,Electronics Store,1299.99,shopping,Miami FL,true
TX004,2025-01-15T14:28:00Z,Luxury Store,2499.00,shopping,Miami FL,true
TX005,2025-01-15T09:15:00Z,Coffee Shop,5.75,food,Seattle WA,false
TX006,2025-01-14T18:30:00Z,Restaurant,67.50,food,Seattle WA,false
TX007,2025-01-14T12:00:00Z,Grocery Store,134.28,groceries,Seattle WA,false
TX008,2025-01-13T19:45:00Z,Movie Theater,32.00,entertainment,Seattle WA,false
TX009,2025-01-13T15:20:00Z,Pharmacy,18.99,healthcare,Seattle WA,false
TX010,2025-01-12T10:30:00Z,Gas Station,48.50,fuel,Seattle WA,false
TX011,2025-01-11T20:15:00Z,Restaurant,89.75,food,Seattle WA,false
TX012,2025-01-11T14:00:00Z,Online Store,156.00,shopping,Seattle WA,false
TX013,2025-01-10T16:45:00Z,Utility Bill,125.00,bills,Seattle WA,false
TX014,2025-01-10T11:20:00Z,Coffee Shop,6.50,food,Seattle WA,false
TX015,2025-01-09T13:00:00Z,Grocery Store,98.45,groceries,Seattle WA,false
fraud_indicators:
geographic_anomaly: true # Miami FL purchases while normal pattern is Seattle WA
rapid_succession: true # Multiple high-value purchases within 3 minutes
unusual_amount: true # $3,798.99 total in 3 minutes vs avg $150/transaction
location_impossible: true # 2,800 miles traveled in 2 hours
task: |
Analyze the flagged transactions and:
1. Assess fraud probability (0-100%)
2. Explain suspicious patterns detected
3. Recommend immediate action (block/allow/verify)
4. Suggest customer verification questionsReal-World Impact:
Financial institutions process millions of fraud detection requests daily. With TOON's 50% token reduction, a bank processing 100,000 fraud checks per day saves approximately $5,000/month in API costs while maintaining comprehensive transaction context for accurate detection.
Example 4: RAG System - Knowledge Base Query
Use Case: Customer support chatbot retrieving relevant documentation to answer technical questions
TOON Format
# RAG context for technical support query
user_query: "How do I configure SSL certificates for my domain?"
user_context:
user_id: "U-34521"
account_type: "business"
technical_level: "intermediate"
current_plan: "professional"
retrieved_documents[5]{doc_id,title,relevance,last_updated,snippet}:
D847,SSL Certificate Installation Guide,0.94,2024-12-15,"To install an SSL certificate: 1. Generate CSR 2. Submit to CA 3. Download certificate files..."
D392,Domain Configuration Best Practices,0.87,2024-11-28,"SSL/TLS configuration requires proper certificate chain. Ensure intermediate certificates are included..."
D615,Troubleshooting SSL Errors,0.82,2024-12-01,"Common SSL errors: Certificate mismatch ERR_CERT_COMMON_NAME_INVALID occurs when domain doesn't match..."
D204,Security Setup Walkthrough,0.76,2024-10-15,"Navigate to Security > SSL/TLS settings. Click Add Certificate. Upload your certificate and private key..."
D981,Automatic SSL with Let's Encrypt,0.71,2024-12-10,"Enable automatic SSL: Go to Domains > Select domain > Enable Auto SSL. System will obtain free certificate..."
previous_conversation[3]{role,message}:
user,"I just bought a new domain"
assistant,"Congratulations! I can help you set up your domain. What would you like to configure first?"
user,"I need to set up HTTPS for security"
instruction: |
Using the retrieved documentation above:
1. Provide a clear, step-by-step answer to the user's question
2. Recommend the easiest SSL option for their technical level
3. Cite specific document IDs where appropriate
4. Offer to help with next steps after SSL setupToken Efficiency in RAG:
RAG systems are prime candidates for TOON optimization. This example includes 5 retrieved documents with metadata:
- • JSON format: ~280 tokens for document metadata
- • TOON format: ~140 tokens for same data
- • Result: Can include 10 documents in TOON vs 5 in JSON (same token budget)
Example 5: Healthcare Clinical Decision Support
Use Case: AI assistant helping physicians analyze patient data and suggest diagnoses
TOON Format
# Clinical decision support - Patient case analysis
patient:
id: "PT-892341"
age: 45
gender: "female"
weight_kg: 68
height_cm: 165
chief_complaint: "Persistent fatigue and joint pain for 3 months"
vital_signs:
blood_pressure: "138/88"
heart_rate: 82
temperature_c: 37.1
respiratory_rate: 16
lab_results[8]{test,value,unit,reference_range,flag}:
Hemoglobin,11.2,g/dL,12.0-16.0,low
WBC Count,12500,cells/mcL,4500-11000,high
ESR,45,mm/hr,0-20,high
CRP,15.2,mg/L,0-5,high
Rheumatoid Factor,42,IU/mL,0-14,high
Anti-CCP,68,units,0-20,high
TSH,2.8,mIU/L,0.4-4.0,normal
Vitamin D,18,ng/mL,30-100,low
symptoms[6]: "fatigue", "joint pain", "morning stiffness", "swelling in hands", "decreased appetite", "weight loss"
medical_history[3]: "hypothyroidism (controlled)", "vitamin D deficiency", "no autoimmune disorders"
current_medications[2]{medication,dosage,frequency}:
Levothyroxine,75mcg,daily
Vitamin D3,2000IU,daily
task: |
Based on patient presentation and lab results:
1. List top 3 differential diagnoses with supporting evidence
2. Recommend additional tests needed for definitive diagnosis
3. Suggest initial treatment approach
4. Identify any urgent concerns requiring immediate attentionHealthcare Applications:
Medical AI assistants need comprehensive patient data for accurate recommendations. TOON's structured arrays for lab results, medications, and symptoms allow clinicians to provide complete context without exceeding token limits, ensuring safer and more accurate AI-assisted diagnoses.
Example 6: Sales Pipeline Forecasting
Use Case: AI-powered sales analytics predicting quarterly revenue and identifying at-risk deals
TOON Format
# Q1 2025 Sales Pipeline Analysis
pipeline_deals[12]{deal_id,company,value,stage,probability,days_in_stage,last_contact,rep}:
D1001,Acme Corp,125000,negotiation,0.80,12,2025-01-14,Sarah M
D1002,TechStart,45000,proposal,0.60,8,2025-01-13,Michael C
D1003,Enterprise Co,340000,contract_review,0.90,5,2025-01-15,Sarah M
D1004,StartupXYZ,28000,discovery,0.30,22,2024-12-28,Jennifer K
D1005,Global Industries,580000,negotiation,0.75,18,2025-01-10,David P
D1006,Innovation Labs,95000,proposal,0.65,10,2025-01-12,Michael C
D1007,Cloud Systems,67000,qualified,0.40,15,2025-01-08,Emma W
D1008,Digital Solutions,156000,negotiation,0.85,7,2025-01-14,Sarah M
D1009,DataFlow Inc,89000,proposal,0.55,12,2025-01-11,Jennifer K
D1010,AgileWorks,234000,contract_review,0.95,3,2025-01-15,David P
D1011,ScaleUp Ltd,45000,discovery,0.35,19,2025-01-05,Emma W
D1012,FutureTech,178000,negotiation,0.70,14,2025-01-13,Michael C
team_performance[5]{rep,deals_closed_q4,quota,current_pipeline,forecast}:
Sarah M,8,500000,623000,498400
Michael C,6,400000,328000,196800
Jennifer K,4,350000,117000,40950
David P,7,450000,814000,651200
Emma W,3,300000,112000,39200
quarter_target: 2000000
days_remaining: 75
task: |
Analyze the sales pipeline and provide:
1. Probability-weighted revenue forecast for Q1
2. Identify deals at risk (stalled >15 days or probability dropping)
3. Recommend which deals to prioritize for each rep
4. Assess if team will hit the $2M quarter target
5. Suggest specific actions to close the gap if neededBusiness Impact:
Sales teams analyze pipeline data multiple times daily. TOON's efficiency allows including complete deal context, team performance metrics, and historical data in a single LLM prompt - enabling comprehensive analysis without hitting token limits.
Example 7: Multi-Turn Chatbot Conversation
Use Case: Customer service chatbot maintaining conversation context across multiple interactions
TOON Format
# Customer service conversation context
session:
session_id: "S-7821934"
started: "2025-01-15T14:20:00Z"
duration_minutes: 8
channel: "web_chat"
customer:
id: "C-34892"
name: "Alex Johnson"
tier: "premium"
account_age_days: 487
conversation[12]{role,message,timestamp,intent}:
user,"Hi I need help with my recent order","14:20:15",order_inquiry
assistant,"Hello Alex! I'd be happy to help. What's your order number?","14:20:18",greeting
user,"Order #ORD-5628","14:20:45",provide_info
assistant,"I found order #ORD-5628 placed on Jan 10 for $234.50. How can I assist?","14:20:52",order_found
user,"It says delivered but I didn't receive it","14:21:30",delivery_issue
assistant,"I'm sorry to hear that. Let me check the tracking details.","14:21:35",acknowledge
user,"The tracking shows it was left at my door","14:22:10",provide_detail
assistant,"I see it was marked delivered on Jan 12 at 2:45 PM. Did you check with neighbors?","14:22:20",troubleshoot
user,"Yes I checked everywhere. Nothing.","14:23:15",negative_response
assistant,"I understand this is frustrating. I can either reship the order or process a full refund.","14:23:25",offer_solution
user,"I'd like a refund please","14:24:00",choose_option
assistant,"Absolutely. I'm processing a $234.50 refund to your original payment method.","14:24:10",action_taken
order_details:
order_id: "ORD-5628"
order_date: "2025-01-10"
items[3]{product,quantity,price}:
Wireless Keyboard,1,79.99
Mouse Pad,2,12.99
USB Hub,1,34.99
shipping_address: "123 Main St, Boston MA 02101"
carrier: "UPS"
tracking: "1Z999AA10123456784"
delivery_status: "delivered"
delivery_date: "2025-01-12T14:45:00Z"
previous_interactions[2]{date,issue,resolution}:
2024-11-20,product_question,answered
2024-09-15,billing_inquiry,resolved
actions_taken[1]{action,amount,status}:
refund_processed,234.50,pending
task: "Continue the conversation. Inform customer about refund timeline and offer discount on next purchase."Conversation Context Efficiency:
Chatbots benefit enormously from TOON's compact conversation history. This example includes 12 conversation turns, order details, and customer history - all in ~180 tokens vs ~360 tokens in JSON. Result: 2x longer conversation context or more customer background within the same token budget.
Example 8: System Log Analysis for Debugging
Use Case: DevOps AI assistant analyzing application logs to diagnose production issues
TOON Format
# Production incident investigation - API latency spike
incident:
incident_id: "INC-2847"
severity: "high"
started: "2025-01-15T10:45:00Z"
duration_minutes: 23
affected_service: "payment-api"
error_logs[10]{timestamp,level,service,message,user_id,response_time_ms}:
10:45:12,ERROR,payment-api,"Database connection timeout",U-4821,8500
10:45:15,ERROR,payment-api,"Database connection timeout",U-3092,8700
10:45:18,WARN,payment-api,"Retry attempt 1 failed",U-4821,9200
10:45:23,ERROR,payment-api,"Transaction rollback",U-5634,8300
10:45:28,ERROR,payment-api,"Database connection timeout",U-7821,8900
10:45:35,ERROR,payment-api,"Maximum retry attempts exceeded",U-4821,15000
10:46:02,ERROR,payment-api,"Database connection timeout",U-2341,8600
10:46:15,WARN,payment-api,"Connection pool exhausted",null,null
10:46:30,ERROR,payment-api,"Service unavailable",U-9102,12000
10:46:45,ERROR,payment-api,"Database connection timeout",U-6723,9100
system_metrics:
cpu_usage: "45%"
memory_usage: "78%"
database_connections:
active: 95
max: 100
waiting: 23
request_rate: "450 req/min"
avg_response_time: "8700ms"
error_rate: "28%"
normal_baseline:
avg_response_time: "120ms"
error_rate: "0.5%"
database_connections_avg: 35
recent_changes[3]{time,type,description}:
2025-01-15T09:30:00Z,deployment,"v2.4.3 deployed to production"
2025-01-15T08:00:00Z,config,"Database pool size increased to 100"
2025-01-14T16:00:00Z,feature,"New payment provider integration"
task: |
Analyze the incident and provide:
1. Root cause analysis based on logs and metrics
2. Immediate mitigation steps to restore service
3. Correlation between recent changes and the incident
4. Long-term fixes to prevent recurrence
5. Postmortem recommendationsDevOps Token Efficiency:
Log analysis often requires hundreds of log entries for accurate diagnosis. TOON's structured arrays let DevOps teams include 2-3x more log entries in LLM prompts, leading to better root cause identification and faster incident resolution.
Token Savings Across Use Cases
| Use Case | JSON Tokens | TOON Tokens | Savings |
|---|---|---|---|
| Customer Churn Analysis | ~420 | ~210 | 50% |
| E-commerce Orders | ~340 | ~170 | 50% |
| Fraud Detection | ~520 | ~260 | 50% |
| RAG Document Retrieval | ~280 | ~140 | 50% |
| Healthcare Records | ~390 | ~195 | 50% |
| Sales Pipeline | ~480 | ~240 | 50% |
| Chatbot Conversation | ~360 | ~180 | 50% |
| Log Analysis | ~440 | ~220 | 50% |
* Token counts measured using GPT-4 tokenizer. Actual savings may vary slightly based on data content.
Try These Examples Yourself
All examples on this page are valid TOON format. You can:
1. Validate Examples
Copy any example above and paste into our TOON Validator to verify syntax
2. Convert to JSON
See the JSON equivalent and compare token counts
3. Convert Your Data
Upload your own JSON and see how TOON reduces token count for your specific use case
4. Visualize Structure
Use TOON Viewer to see tabular data in an interactive table format
Related Resources
External Resources
- •TOON Official GitHub - More examples and code samples
- •OpenAI Tokenizer - Test token counts for your data
- •OpenAI API Pricing - Calculate real cost savings with TOON
- •Anthropic Claude API - Use these examples with Claude
- •OpenAI Text Generation Guide - Best practices for LLM applications
- •What is TOON Format? - Introduction and basics
- •TOON Format Specification - Complete syntax reference