Prompts are code. They determine the behavior of AI systems as precisely as source code determines the behavior of traditional software. But while source code has file formats, version control, linters, CI/CD pipelines, and standards — prompts have... strings pasted into text boxes. The .sinc.json file format changes this.
In most AI applications today, prompts are hardcoded strings embedded in application code or stored in unstructured text files. This creates several problems:
The .sinc.json file format is a standardized schema for structured prompts based on sinc-LLM's 6-band decomposition. Every .sinc.json file contains:
{
"formula": "x(t) = \u03a3 x(nT) \u00b7 sinc((t - nT) / T)",
"T": "specification-axis",
"fragments": [
{"n": 0, "t": "PERSONA", "x": "..."},
{"n": 1, "t": "CONTEXT", "x": "..."},
{"n": 2, "t": "DATA", "x": "..."},
{"n": 3, "t": "CONSTRAINTS", "x": "..."},
{"n": 4, "t": "FORMAT", "x": "..."},
{"n": 5, "t": "TASK", "x": "..."}
]
}
The schema is fixed. The content varies. This makes .sinc.json files validatable, diffable, testable, and portable.
The 6-band structure is not arbitrary. It is derived from the Nyquist-Shannon sampling theorem applied to specification signals. Your intent is a continuous signal with energy across 6 frequency bands. Each band captures a distinct specification dimension that the LLM needs to reconstruct your intent without aliasing (hallucination).
Just as audio has a fixed number of audible frequency ranges, specifications have a fixed number of semantic dimensions: who (PERSONA), where (CONTEXT), what input (DATA), what rules (CONSTRAINTS), what output (FORMAT), and what action (TASK). These 6 dimensions are necessary and sufficient for complete specification.
With .sinc.json files, prompts become first-class citizens in your repository:
# Directory structure prompts/ generate-report.sinc.json analyze-data.sinc.json write-email.sinc.json review-code.sinc.json
Git diffs show exactly which band changed:
- {"n": 3, "t": "CONSTRAINTS", "x": "Maximum 500 words"}
+ {"n": 3, "t": "CONSTRAINTS", "x": "Maximum 800 words. Include citations."}
This is infinitely more reviewable than a diff of two blobs of text. The reviewer can see immediately: the CONSTRAINTS band changed, length increased, citations added. No other bands were affected.
With a standardized schema, you can validate prompts in your CI/CD pipeline:
# GitHub Actions example
- name: Validate prompts
run: |
for f in prompts/*.sinc.json; do
# Check schema validity
python -c "import json; d=json.load(open('$f')); assert len(d['fragments'])==6; assert all(f['t'] in ['PERSONA','CONTEXT','DATA','CONSTRAINTS','FORMAT','TASK'] for f in d['fragments'])"
done
This validation catches missing bands before they reach production. A prompt without a CONSTRAINTS band is rejected at CI — it never gets deployed.
Beyond schema validation, .sinc.json files can be linted for quality:
A .sinc.json file works with every LLM because the 6-band structure captures universal specification dimensions, not model-specific features. The same file can be sent to ChatGPT, Claude, Gemini, Llama, or Mistral. The interpretation may vary slightly by model, but the specification is complete for all of them.
This is the key advantage over model-specific prompt formats. OpenAI's structured outputs use JSON Schema for output format — but that is model-specific and covers only the FORMAT dimension. sinc-LLM's .sinc.json covers all 6 dimensions and works everywhere.
The .sinc.json format enables editor support:
Prompts will be treated like code within 2 years. They will have file formats, linters, test suites, version control, and CI/CD pipelines. The question is not whether this will happen — it is which schema will become the standard.
The .sinc.json format has three advantages: it is based on mathematical theory (not arbitrary categories), it is minimal (6 bands, no bloat), and it works with every model (no vendor lock-in). The DOI-registered specification ensures the format will not change arbitrarily.
{
"formula": "x(t) = \u03a3 x(nT) \u00b7 sinc((t - nT) / T)",
"T": "specification-axis",
"fragments": [
{"n": 0, "t": "PERSONA", "x": "Expert data scientist with 10 years ML experience"},
{"n": 1, "t": "CONTEXT", "x": "Building a recommendation engine for an e-commerce platform"},
{"n": 2, "t": "DATA", "x": "Dataset: 2M user interactions, 50K products, sparse matrix"},
{"n": 3, "t": "CONSTRAINTS", "x": "Must use collaborative filtering. Latency under 100ms. No PII in logs. Python 3.11+. Must handle cold-start users with content-based fallback"},
{"n": 4, "t": "FORMAT", "x": "Python module with type hints, docstrings, and pytest tests"},
{"n": 5, "t": "TASK", "x": "Implement the recommendation engine with train/predict/evaluate methods"}
]
}
Start using .sinc.json today. Create your first structured prompt at sincllm.com and save it as a .sinc.json file in your repository. Your prompts deserve the same engineering rigor as your code.