YAML vs TOON: Human-Readable Format Battle for LLM Optimization

In the pantheon of data serialization, YAML (YAML Ain't Markup Language) stands as the undisputed king of human readability. It conquered the world of DevOps, becoming the standard for Kubernetes, Ansible, and GitHub Actions. But in the age of Generative AI, where data is read by machines more often than humans, strict readability can become a liability.

TOON approaches data from a different angle. It asks: "What if we kept the whitespace-friendly nature of YAML for structure, but optimized the data density for Tokenizers?" The result is a format that feels familiar to YAML developers but performs drastically better for LLM Context Windows.

The Token Tax: Arrays of Objects

The biggest difference comes when representing lists of data—the bread and butter of RAG context.

YAML is row-oriented and repetitive. To list three users, you must repeat the keys id, name, and role three times.

users:
  - id: 1
    name: Alice
    role: admin
  - id: 2
    name: Bob
    role: user
  - id: 3
    name: Charlie
    role: user

Token Count: ~60 tokens.
Every repetition - id: costs tokens. In a list of 100 items, you pay this tax 100 times.

TOON is Header-Oriented (like CSV) but nested (like JSON). It defines the schema once.

users[3]{id,name,role}:
  1, Alice, admin
  2, Bob, user
  3, Charlie, user

Token Count: ~35 tokens.
Savings: ~40%.

This "Header Row" optimization allows TOON to combine the readability of YAML with the density of CSV. For an LLM with a 128k context window, this 40% saving translates to 40% more data you can fit in the prompt.

Advanced Literals: Handling Long Strings

One of YAML's best features is its handling of long strings (like prompt templates or code snippets) using Block Scalars. TOON respects this legacy and adopts a similar but simplified approach.

YAML: The confusing | vs >

YAML has two symbols for blocks:
| (Literal Style): keeps newlines.
> (Folded Style): replaces newlines with spaces.
Plus modifiers: |-, |+, >-. It's hard to remember which is which.

TOON: The Triple-Quote '''

TOON borrows from Python/Markdown. Just use triple quotes. It's universally understood.

description: '''
This is a long text block.
It preserves newlines exactly as written.
No complex modifiers needed.
'''

Benchmarks: The Real Cost of "Clean"

We ran a benchmark comparing YAML and TOON on a variety of industry-standard datasets.

The takeaway: If your data is a single object (like a K8s config), YAML is fine (only 7% worse). If your data is a List of Objects (like most AI context), YAML is disastrously inefficient (50% worse).

Safety: The yaml.load() Vulnerability

A hidden danger of YAML is that in many languages (like Python and Ruby), the default load() function is unsafe. It can instantiate arbitrary objects and execute code.

!!python/object/apply:os.system
args: ['rm -rf /']

If you blindly parse untrusted YAML, you are vulnerable to RCE (Remote Code Execution).

TOON is data-only. It has no mechanism to instantiate classes or call functions. It is safe by design to parse untrusted TOON input from an LLM or user.

When to Stick with YAML

Despite TOON's advantages, YAML is not going anywhere.

• Ecosystem Compatibility: If you are writing a github-action.yml, you must use YAML.
• Short Configs: For a 10-line config file, the overhead of defining header rows in TOON (users[]{...}) might feel like overkill.
• Anchors/Aliases: If you rely heavily on &base_config and <<: *base_config to reduce duplication in manual editing, YAML wins.