How to Fix AI Hallucination: The Nyquist Approach

Why Current Fixes Do Not Work

Retrieval, training, prompt design and output checks address different parts of an AI workflow. Evaluate errors against the actual task and evidence before selecting a correction.

The author proposes specification aliasing as an analogy for missing prompt details. It is a hypothesis to examine, not an established general cause of hallucination.

Hallucination as Aliasing

When a signal is sampled too slowly, the output contains fake frequencies. These look real, but they were never in the original signal. That is called aliasing.

The six-field method asks reviewers to identify unstated requirements. Missing details can lead to assumptions; counting fields does not establish whether an answer is factual.

The connection is not just a metaphor. The math is the same. And the fix is the same. I show this in the paper: sample at or above the Nyquist rate.

Six-Field Prompt Review

Use these steps to review task instructions before generation:

1. Decompose every prompt into 6 specification bands: PERSONA, CONTEXT, DATA, CONSTRAINTS, FORMAT, TASK
2. Fill each specification field explicitly, including CONSTRAINTS.
3. Give constraints and format enough detail to express the task requirements.
4. Verify that no band is left empty for the model to guess

Review all six fields for relevant instructions, then check the response against requirements and reliable sources. A complete-looking prompt does not guarantee factual output.

Prompt-Review Method

Use the following field-coverage groups when designing a prompt comparison; no completed experiment or measured hallucination rate is established here:

• 1–2 fields supplied: record missing task details and evaluate the response.
• 3–4 fields supplied: record remaining gaps and evaluate the response.
• 5–6 fields supplied: check instruction completeness and factual accuracy separately.

Compare prompts on the intended workload to determine whether adding a field changes output quality or error rates. Review constraints and format without assigning them an unmeasured causal advantage.

Practical Implementation

There are three ways to apply this fix:

1. Manual Decomposition

Before you send a prompt, ask: does it fill all 6 bands? If not, add what is missing.

2. Auto-Scatter Tool

Use the free sinc-LLM transformer to break down your prompt and find missing bands automatically.

3. Framework Integration

Add my sinc-LLM framework to your pipeline. It checks every prompt for 6-band compliance automatically.

Review the prompt before generation and check the resulting output afterward. The linked paper describes the author’s proposed analogy; it does not establish prevention of hallucination.

{
  "formula": "x(t) = Σ x(nT) · sinc((t - nT) / T)",
  "T": "specification-axis",
  "fragments": [
    {
      "n": 0,
      "t": "PERSONA",
      "x": "You are a AI reliability engineer. You provide precise, evidence-based analysis with exact numbers and no hedging."
    },
    {
      "n": 1,
      "t": "CONTEXT",
      "x": "This analysis is part of a production system where accuracy determines revenue. The sinc-LLM framework identifies 6 specification bands with six named fields."
    },
    {
      "n": 2,
      "t": "DATA",
      "x": "Example allocation assumptions: CONSTRAINTS=42.7%, FORMAT=26.3%, PERSONA=7.0%, CONTEXT=6.3%, DATA=3.8%, TASK=2.8%. SNR formula: 0.588 + 0.267 * G(Z1) * H(Z2) * R(Z3) * G(Z4). These allocations are illustrative assumptions, not observed quality weights."
    },
    {
      "n": 3,
      "t": "CONSTRAINTS",
      "x": "State facts directly. Never hedge with 'I think' or 'probably'. Use exact numbers for every claim. Do not suggest generic solutions. Every recommendation must be specific and verifiable. Include at least 3 MUST/NEVER rules specific to this task."
    },
    {
      "n": 4,
      "t": "FORMAT",
      "x": "Lead with the definitive answer. Use structured headers. Tables for comparisons. Numbered lists for sequences. Code blocks for implementations. No trailing summaries."
    },
    {
      "n": 5,
      "t": "TASK",
      "x": "Fix a medical chatbot that hallucinates drug interactions 15% of the time by adding missing specification bands"
    }
  ]
}