Using ZON with LLMs

Real-World Scenarios

1. The "Context Window Crunch"

Scenario: You need to pass 50 user profiles to GPT-4 for analysis.

• JSON: 15,000 tokens. (Might hit limits, costs $0.15)
• ZON: 10,500 tokens. (Fits easily, costs $0.10)
• Impact: 30% cost reduction and faster latency.

2. The "Complex Config"

Scenario: Passing a deeply nested Kubernetes config to an agent.

• CSV: Impossible.
• YAML: 2,000 tokens, risk of indentation errors.
• ZON: 1,400 tokens, robust parsing.
• Impact: Zero hallucinations on structure.

LLM Retrieval Accuracy Testing

Methodology

ZON achieves 100% LLM retrieval accuracy through systematic testing:

Test Framework: benchmarks/retrieval-accuracy.js

Process:

1. Data Encoding: Encode 27 test datasets in multiple formats (ZON, JSON, TOON, YAML, CSV, XML)
2. Prompt Generation: Create prompts asking LLMs to extract specific values
3. LLM Querying: Test against GPT-4o, Claude, Llama (controlled via API)
4. Answer Validation: Compare LLM responses to ground truth
5. Accuracy Calculation: Percentage of correct retrievals

Datasets Tested:

• Simple objects (metadata)
• Nested structures (configs)
• Arrays of objects (users, products)
• Mixed data types (numbers, booleans, nulls, strings)
• Edge cases (empty values, special characters)

Validation:

• Token efficiency measured via gpt-tokenizer
• Accuracy requires exact match to original value
• Tests run on multiple LLM models for consistency

Results: 100% accuracy across all tested LLMs and datasets

Run Tests:

node benchmarks/retrieval-accuracy.js

Output: accuracy-results.json with per-format, per-model results