Tested across Claude, GPT-4, DeepSeek, and Llama 3.
The key finding: LLMs handle tabular formats natively because they've seen billions of markdown tables and CSVs in training.
No special prompting needed.
For associations, I tested with multi-table ISON docs like:
table.users
id name
1 Alice
2 Bob
table.orders
id user_id product
101 :1 Widget
102 :2 Gadget
Prompt: "What did Alice order?"
All models correctly resolved :1 → Alice → Widget without explicit instructions about the reference syntax.
The 30-70% token savings come from removing JSON's structural overhead (braces, quotes, colons, commas) while keeping the same semantic density.
Haven't published formal benchmarks on this yet - that's good feedback. I should.
ISON: 88.3% accuracy JSON: lower (can share exact numbers if interested)
Tested across Claude, GPT-4, DeepSeek, and Llama 3.
The key finding: LLMs handle tabular formats natively because they've seen billions of markdown tables and CSVs in training. No special prompting needed.
For associations, I tested with multi-table ISON docs like:
table.users id name 1 Alice 2 Bob
table.orders id user_id product 101 :1 Widget 102 :2 Gadget
Prompt: "What did Alice order?"
All models correctly resolved :1 → Alice → Widget without explicit instructions about the reference syntax.
The 30-70% token savings come from removing JSON's structural overhead (braces, quotes, colons, commas) while keeping the same semantic density.
Haven't published formal benchmarks on this yet - that's good feedback. I should.