CSV vs TOON: Which Format for Your LLM Data?

CSV (Comma-Separated Values) is the most successful data format in history. It connects mainframes to cloud databases, Excel to Python, and analysts to engineers. It is concise, readable, and universally supported.

But for Large Language Models (LLMs), CSV presents a unique set of challenges. Its lack of type information, inability to handle nested data, and semantic ambiguity can confuse even the smartest models. TOON was built to solve these specific problems while retaining the tabular efficiency that makes CSV great.

The Untyped Giant vs The Typed Successor

CSV is "Stringly Typed." Everything is a string, composed of characters between commas.

id,title,is_published
101,The Great Gatsby,1

To a human, 101 is a number, and 1 (is_published) is a boolean. To an LLM (or a parser), they are just text.

• Is 101 an integer ID or a string identifier "101"?
• Is 1 the number one, or does it mean True?

This ambiguity significantly increases Hallucination Risk. The LLM has to waste attention heads inferring types from context.

TOON: Semantic Clarity

books[1]{id:u32, title:str, is_published:bool}:
  101, The Great Gatsby, true

In TOON, types are explicit. u32 tells the model "This is an unsigned integer." bool tells the model "This is a logical flag." This reduces cognitive load for the model, allowing it to focus on reasoning rather than parsing.

The Nesting Problem: Flattening Hell

Real-world data is rarely flat. An Order has LineItems. A User has Addresses.

CSV Approach: Flattening

order_id,item_1_name,item_1_price,item_2_name,item_2_price

This "wide format" is brittle and rigid. What if there are 3 items? You have to change the schema manually.

TOON Approach: Native Nesting

orders[1]{id, items:list<Item>}:
  101, [{name:"Book", price:10}, {name:"Pen", price:2}]

TOON handles complexity gracefully. This preserves the Semantic Hierarchy of the data, which is crucial for RAG performance (retrieving the right context).

Deep Dive: The "Ragged Row" Problem

What happens when data is missing? In CSV, if a row is missing a value, you get ,,.

id,name,email
1,Alice,alice@example.com
2,Bob,

Does the missing email mean "No Email" (Null) or "Empty String" (Verified but blank)? Or did the export script crash?

TOON supports precise null handling and optional fields.

users[2]{id, name, email?}:
  1, Alice, alice@example.com
  2, Bob, null

The ? marks the field as optional. The null keyword is explicit.

Token Economics: The 1% Difference

If we look purely at file size, CSV often wins by a tiny margin because it uses single-character delimiters , and newlines. TOON uses , and [] headers.

The Verdict: CSV is ~1% cheaper on tokens.

However: This 1% saving costs you 100% of your type safety. For an LLM, the "Cost of Confusion" (generating a wrong answer because it misread a string as a number) is vastly higher than the cost of 15 tokens. TOON is the "Smart Token" choice.

Safety Features: The Injection Vulnerability

As discussed in our Understanding CSV guide, CSV is vulnerable to Formula Injection (=cmd|' /C calc'!A0).

TOON is explicitly safe. It defines no executable syntax (like formulas). It is strictly a data serialization format. When you build an AI Agent that reads user-uploaded data, converting it to TOON first sanitizes it effectively against Excel-targeting attacks.

When to Use Which?

Stick with CSV if:

• You are talking to Excel: Business users live in spreadsheets.
• You are migrating legacy data: SQL dumps are usually CSV.
• You are doing basic Data Science: The Python pandas library is optimized for CSV.

Switch to TOON if:

• You are Prompt Engineering: You need an LLM to output structured data reliably.
• You have Nested Data: You have lists inside your objects.
• You need Types: You care about 1 vs "1".
• You are building RAG: Chunk retrieval needs clear semantic boundaries.

Conclusion

CSV is concise but dumb. It holds data, but it doesn't describe it.

TOON is concise and smart. It holds data and describes exactly what that data is.

In the era of "Intelligence as a Service," we cannot afford to be dumb about our data.