Engineering reference

sinc-LLM: Transform Any Prompt Into 6 Nyquist-Compliant Bands

A prompt-decomposition reference showing the structured band format and how the transformation interface is organized.

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x(t) = Σ x(nT) · sinc((t - nT) / T)

sinc-LLM

Organize raw prompts into six named fields. Review the transformed instructions and check the resulting model output.

Paper (Zenodo) GitHub Token Calculator

Raw Prompt Input

Paste your raw prompt here... Example: You are a senior Python developer. Analyze this codebase for security vulnerabilities. Focus on SQL injection and XSS. Output a table with file, line, vulnerability type, severity, and fix. Never include false positives. Always show the vulnerable code snippet.

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Signal-to-Noise Analysis

SNR Score

Z1 (Bands)

Z2 (Constraint%)

Z3 (Tokens)

Z4 (Imperatives)

sinc JSON Output

How sinc decomposition works

Nyquist Sampling

The author uses signal sampling as an analogy for prompt structure. The six fields organize task details; the analogy does not establish a hallucination mechanism or a correctness guarantee.

Band Detection

The transformer scans your prompt for markers of each band: persona cues ("you are", "expert"), constraints ("must", "never"), format directives ("table", "structured"), and task verbs ("analyze", "build").

Reconstruction

Missing bands are filled with smart templates derived from detected context. The sinc interpolation formula reconstructs the full 6-band signal, eliminating aliasing artifacts.

SNR Scoring

Four zone functions (G, H, R, G) evaluate band count, constraint density, token budget and imperative count. The configured composite ranges from a baseline of 0.588 to approximately 0.855. These formula values are not validated predictions of output quality.

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