JSON Prompt Template — The .sinc.json Standard for Structured Prompts

The Problem with Text Prompts

Prompts stored as plain text strings cannot be versioned meaningfully. A git diff between two versions of a 500-word prompt shows the entire string changed, even if you only modified one constraint. There is no way to run automated validation — is the prompt missing a persona? Are the constraints specific enough? Is the format specified?

The .sinc.json format treats prompts as structured data with a defined schema. Each specification dimension is a separate field, enabling targeted diffs, automated validation, band-level testing, and machine-to-machine prompt passing in multi-agent systems.

This format was developed by sinc-LLM based on the Nyquist-Shannon sampling theorem applied to natural language specification:

The .sinc.json Schema

{
  "formula": "x(t) = Σ x(nT) · sinc((t - nT) / T)",
  "T": "specification-axis",
  "fragments": [
    {"n": 0, "t": "PERSONA",     "x": "string — who the LLM should be"},
    {"n": 1, "t": "CONTEXT",     "x": "string — background information"},
    {"n": 2, "t": "DATA",        "x": "string — specific inputs and examples"},
    {"n": 3, "t": "CONSTRAINTS", "x": "string — rules and limitations (longest band)"},
    {"n": 4, "t": "FORMAT",      "x": "string — expected output structure"},
    {"n": 5, "t": "TASK",        "x": "string — the action to perform"}
  ]
}

Schema Field Reference

Why JSON Instead of Plain Text?

Version control: When you change a constraint, the git diff shows only the CONSTRAINTS band changed. Not the entire prompt. This makes prompt evolution traceable and reviewable.

Validation: A JSON schema validator can check that all 6 bands are present, that the CONSTRAINTS band exceeds a minimum length threshold, and that no band is empty. You can add this to CI/CD pipelines with a pre-commit hook.

Machine readability: In multi-agent systems, agents need to pass specifications to each other. The sinc JSON format is the inter-agent cognitive contract — each agent reads the bands it needs without parsing natural language.

Diffability: Compare two prompt versions side by side at the band level. See exactly which specification dimension changed between versions.

Composability: Merge bands from different prompts. Use the PERSONA from one prompt, the CONSTRAINTS from another, and the FORMAT from a third. This is trivial with JSON fields and impossible with text paragraphs.

File Convention

sinc-LLM recommends the .sinc.json file extension for structured prompts. This makes prompts discoverable and enables editor plugins, linters, and CI rules to target prompt files specifically.

# Project structure
prompts/
  customer-support.sinc.json
  code-review.sinc.json
  content-writer.sinc.json
  data-analysis.sinc.json

Each file is a valid JSON document containing the full sinc schema. Store them alongside your application code and version them with git.

Validation with GitHub Actions

# .github/workflows/validate-prompts.yml
name: Validate sinc prompts
on: [push, pull_request]
jobs:
  validate:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - name: Validate sinc JSON
        run: |
          for f in prompts/*.sinc.json; do
            python3 -c "
import json, sys
with open('$f') as fh:
    d = json.load(fh)
assert d.get('formula'), 'Missing formula'
assert d.get('T') == 'specification-axis', 'Invalid T'
assert len(d.get('fragments', [])) == 6, 'Need exactly 6 bands'
bands = {f['t'] for f in d['fragments']}
required = {'PERSONA','CONTEXT','DATA','CONSTRAINTS','FORMAT','TASK'}
assert bands == required, f'Missing bands: {required - bands}'
constraints = next(f for f in d['fragments'] if f['t']=='CONSTRAINTS')
assert len(constraints['x']) >= 100, 'CONSTRAINTS too short'
print(f'PASS: $f')
"
          done

This CI pipeline validates that every .sinc.json file in your repository has all 6 bands, a valid formula, and a CONSTRAINTS band of sufficient length. Failed validation blocks the PR merge.

Using .sinc.json in Your Application

# Python: Load and flatten sinc JSON for any LLM API
import json

def load_sinc(path: str) -> str:
    """Load a .sinc.json file and flatten to a system prompt string."""
    with open(path) as f:
        sinc = json.load(f)
    return "\n\n".join(
        f"[{frag['t']}]\n{frag['x']}"
        for frag in sorted(sinc["fragments"], key=lambda f: f["n"])
    )

# Use with OpenAI
system_prompt = load_sinc("prompts/customer-support.sinc.json")
response = client.chat.completions.create(
    model="gpt-4o",
    messages=[
        {"role": "system", "content": system_prompt},
        {"role": "user", "content": user_message}
    ]
)

The sinc-LLM website generates .sinc.json output that you can copy directly into your project. Visit the full specification for detailed schema documentation.

The Standard for Prompt Engineering Teams

As prompt engineering becomes a professional discipline, teams need a standard format for sharing, reviewing, and versioning prompts. The .sinc.json format provides this standard — it is open, documented, and backed by the signal-theoretic foundation that ensures every prompt captures the full bandwidth of human intent.

The sinc-LLM JSON prompt template generator creates valid .sinc.json files from any raw text input, making it easy to adopt the format without learning the schema manually.