sinc-LLMEngineering reference
sinc-prompt v1.0
The sinc-prompt format reference, including its band definitions, JSON structure, worked example, and supporting resources.
Methods, definitions, and examples for the topic discussed here. Check applicability against the task and operating conditions.
An open format specification for structured LLM prompts, grounded in the Nyquist-Shannon sampling theorem.
The Formula
sinc-prompt Specification v1.0: Structured LLM Prompt Format
x(t) = Σ x(nT) · sinc((t − nT) / T)The sinc-prompt format defines six task-specification fields using a signal-processing analogy. Omitting a field leaves a task detail unstated; the analogy does not establish a mathematical cause of hallucination or guarantee reconstruction.
The 6 Mandatory Bands
| n | Band | Zone | Illustrative allocation, not measured quality weight | Description |
|---|---|---|---|---|
| 0 | PERSONA |
Z1 | 7.0% | Who should answer. The expert identity. Gates the amplitude term via G(Z1): binary presence function, smoothly transitions 0→1 between 2-5 tokens. |
| 1 | CONTEXT |
Z2 | 6.3% | Situation, background, facts and dates. These feed the configured H(Z2) function. Its parameter values are design choices, not validated descriptions of model attention or output quality. |
| 2 | DATA |
Z2 | 3.8% | Specific inputs, metrics, data points. Combined with CONTEXT into zone Z2 for the Hill function. |
| 3 | CONSTRAINTS |
Z3 | 42.7% | Rules, MUST/NEVER directives and boundaries. The configured R(Z3) ramp is zero below 37 tokens, grows at 3% per token above the threshold and saturates at 1.0. This describes score arithmetic, not measured model behavior or a requirement to make this the longest fragment. |
| 4 | FORMAT |
Z4 | 26.3% | Output structure specification. Gates the amplitude term via G(Z4): binary presence function identical to Z1. |
| 5 | TASK |
Z3 | 2.8% | The objective. Clear imperative. Combined with CONSTRAINTS into zone Z3 for the ramp function. |
SNR Model
This specification defines the following SNR score for its prompt format. It is an input design measure, not a verified estimate of factual correctness or output quality.
Zone Functions
| Function | Zone | Definition | Behavior |
|---|---|---|---|
G(x) |
Z1, Z4 | clamp((x, 2) / 3, 0, 1) |
Binary gate. Absent below 2 tokens, fully present above 5. |
H(z) |
Z2 | (z/94)^4.56 / (1+(z/94)^4.56) * exp(-1.36e-5*(z-94)^2) |
Hill function. Peaks at 94 tokens, sigmoidal rise, Gaussian decay. |
R(z) |
Z3 | clamp(0.03 * max(0, z, 37), 0, 1) |
Configured ramp arithmetic: zero below 37 tokens, +3% per token, saturating at 1.0. These constants do not establish output-quality behavior. |
Defined score range: 0.588–0.855. The specification’s labels EXCELLENT (≥0.80), GOOD (≥0.70), ADEQUATE (≥0.65), ALIASED (≥0.60), and CRITICAL (<0.60) describe this input score; they do not validate the model’s output.
Nyquist Rationale
The specification uses the Nyquist-Shannon theorem as an analogy for organizing task details into six fields. Its gate-function SNR formula describes the proposed format’s design. Neither the analogy nor a positive gate score establishes correct, hallucination-free, or instruction-compliant output.
JSON Structure
{
"formula": "x(t) = Sigma x(nT) * 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": "... (longest fragment)"},
{"n": 4, "t": "FORMAT", "x": "..."},
{"n": 5, "t": "TASK", "x": "..."}
]
}
Required Fields
| Field | Type | Description |
|---|---|---|
formula | string | The sinc reconstruction formula. |
T | string | Sampling axis identifier. |
fragments | array | Array of 6 fragment objects (7 with optional TASK_ARCHIVED). |
fragments[].n | integer 0-5 | Band index. |
fragments[].t | string enum | Band name: PERSONA, CONTEXT, DATA, CONSTRAINTS, FORMAT, or TASK. |
fragments[].x | string (non-empty) | Band content. |
Complete Example
{
"formula": "x(t) = Sigma x(nT) * sinc((t - nT) / T)",
"T": "specification-axis",
"fragments": [
{
"n": 0,
"t": "PERSONA",
"x": "You are a senior fintech compliance analyst with 10 years of experience in payment processing regulations across EU and US jurisdictions."
},
{
"n": 1,
"t": "CONTEXT",
"x": "PSD2 Strong Customer Authentication enforcement began September 2019 in the EU. The US has no equivalent federal mandate but NACHA updated ACH rules in March 2024. Our platform processes 2.3M transactions/month across both regions."
},
{
"n": 2,
"t": "DATA",
"x": "Current SCA exemption rate: 34%. Transaction decline rate post-SCA: 12.7%. Average transaction value: EUR 47.20. TRA threshold: EUR 500. Low-value threshold: EUR 30."
},
{
"n": 3,
"t": "CONSTRAINTS",
"x": "State facts directly -- never hedge with 'I think', 'probably', 'might'. Use exact regulatory article numbers for every claim (e.g., PSD2 Article 97(1)(c)). Never conflate EU and US requirements in the same paragraph. Always specify which jurisdiction a rule applies to. Must include effective dates for every regulation cited. Never recommend a compliance approach without citing the specific exemption clause. Always quantify financial impact using the provided transaction data. Do not speculate on future regulatory changes -- state only enacted rules."
},
{
"n": 4,
"t": "FORMAT",
"x": "Lead with a 3-row summary table: Requirement | EU (PSD2) | US (NACHA). Then structured sections with H2 headers per topic. Numbered lists for action items. Code blocks for any API payload examples. No parenthetical asides. No trailing summary."
},
{
"n": 5,
"t": "TASK",
"x": "Produce a compliance gap analysis comparing our current SCA implementation against PSD2 RTS requirements and NACHA 2024 ACH rules. Identify the top 5 gaps ranked by financial risk using our transaction data."
}
]
}
Resources
Scope and context
sincllm About Blog Services
sinc-prompt specification v1.0 — Mario Alexandre — DOI: 10.5281/zenodo.19152668 — MIT License
sinc-promptv1.0