Prompts are code. They shape what an AI does, just as precisely as source code shapes what traditional software does. Source code has file formats, version control, linters, CI/CD pipelines, and standards. Prompts have none of that; they are strings pasted into text boxes. The .sinc.json file format changes that.
Most AI apps today store prompts as plain text strings inside code files. This causes real problems.
The .sinc.json file format is a standard schema for structured prompts. It is built on sinc-LLM's 6-band breakdown. Every .sinc.json file holds:
{
"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 structure never changes. Only the content changes. This makes .sinc.json files easy to validate, diff, test, and reuse on any model.
The 6-band structure is not a random choice. It comes from the Nyquist-Shannon sampling theorem applied to prompts. Think of your intent as a signal. That signal carries energy in 6 frequency bands. Each band captures one key dimension. The LLM needs all 6 to rebuild your intent without guessing, which is what causes hallucination.
Audio has a fixed set of frequency ranges you can hear. Prompts have a fixed set of meaning dimensions too. Those are: who speaks (PERSONA), the setting (CONTEXT), the input (DATA), the rules (CONSTRAINTS), the output shape (FORMAT), and the job to do (TASK). All 6 are needed. None can be dropped.
With .sinc.json files, prompts live in your code repository just like any other file:
# 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 was edited:
- {"n": 3, "t": "CONSTRAINTS", "x": "Maximum 500 words"}
+ {"n": 3, "t": "CONSTRAINTS", "x": "Maximum 800 words. Include citations."}
This is far easier to review than two blobs of changed text. The reviewer sees right away: the CONSTRAINTS band changed, the length went up, citations were added. Nothing else was touched.
A standard schema lets you check prompts inside your CI/CD pipeline before they ship:
# 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 check catches missing bands before they go live. A prompt with no CONSTRAINTS band fails at CI. It never reaches production.
You can also lint .sinc.json files for quality beyond basic structure checks:
A .sinc.json file works with every LLM. The 6-band structure covers universal specification dimensions, not features tied to one model. You can send the same file to ChatGPT, Claude, Gemini, Llama, or Mistral. Each model may interpret it slightly differently, but the specification is complete for all of them.
This is the big advantage over model-specific formats. OpenAI's structured outputs use JSON Schema for the output shape, but that is tied to one provider and covers only the FORMAT dimension. sinc-LLM's .sinc.json covers all 6 dimensions and works everywhere.
The .sinc.json format lets editors help you write prompts:
Prompts will be treated like code within 2 years. They will have file formats, linters, test suites, version control, and CI/CD pipelines. The only open question is which schema will win.
The .sinc.json format has three advantages. First, it comes from math theory, not guesswork. Second, it is small (6 bands, no extra bloat). Third, it works with every model and locks you into no vendor. The DOI-registered specification means the format will not change without a reason.
{
"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. Build your first structured prompt at sincllm.com and save it as a .sinc.json file in your repo. Your prompts deserve the same care as your code.
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