# Targetlytics - /PLATFORM/DIAGNOSIS-ENGINE (en)

*Generated for AI LLM consumption*

## Section: DiagnosisEngine

### meta

**title**: Diagnosis Engine | AI Hallucination Detection

**description**: Identify and eliminate AI hallucinations with Targetlytics' Diagnosis Engine. Our N-Sampling architecture ensures 99.9% consistency across LLM responses.

### hero

**badge**: Engine v2.4 Active

**title**: Diagnosis Engine: The Core of Visibility

**description**: Deep dive into our N-Sampling & Consistency architecture. Understand how we mitigate AI hallucinations through rigorous statistical analysis and multi-model verification.

#### stats

**threads**: Parallel Threads

**consistency**: Consistency Rating

**latency**: Latency Overhead

### architecture

**title**: N-Sampling Architecture

**description**: Our engine takes a single prompt and executes it across N parallel instances to determine statistical variance and probability distribution. This eliminates the "lucky guess" factor of LLMs.

#### steps

##### input

**label**: Input Injection

**sub**: Single Prompt

##### parallel

**label**: Parallel Execution

**sub**: L = 90 to 128

##### vector

**label**: Vector Analysis

**sub**: Semantic Distance

##### output

**label**: Verified Output

**sub**: Reliability Score

### consistency

**title**: Consistency & Variance Checks

**description**: We measure the semantic distance between N outputs. A lower variance indicates high confidence and factuality, while high variance suggests hallucination or ambiguity.

**heatmap**: Token Probability Heatmap

#### metrics

**title**: Performance Metrics

**standard**: Standard LLM (Zero-shot)

**chain**: Chain-of-Thought

**sampling**: N-Sampling Engine

### output

**title**: Live Diagnosis Output

**description**: Understanding the "why" behind an AI's decision is just as important as the decision itself. Our platform demystifies the black box by breaking down every diagnosis into understandable components.

**intro**: Below, we interpret the raw data generated during a diagnosis cycle. This summary highlights how we derive a verified answer, quantify our certainty, and proactively filter out incorrect information to protect your brand's reputation.

**button**: Download Raw Diagnosis Data

#### cards

##### aggregated

**title**: Aggregated Response

**description**: This is the "consensus truth." Instead of relying on a single AI prediction, we aggregate hundreds of outputs. The response you see is the one that statistically dominates the sample set, ensuring it represents the most accurate and widely held view.

**badge**: Consensus Achieved

##### confidence

**title**: Confidence Score

**description**: We don't just guess; we measure certainty. A score like 98.4% indicates that nearly all independent AI threads agreed on the answer. High confidence scores verify that the result is stable, factual, and not a random hallucination.

**label**: Reliability

##### hallucination

**title**: Hallucination Handling

**description**: When an AI model invents false information, we call it a "hallucination." Our engine detects these anomalies because they fail to align with the majority. These outliers are automatically flagged, isolated, and removed from your final report.

**badge**: 2 Outliers Blocked

### scenario

**title**: Scenario Analysis: Brand Reputation

**description**: See how the Diagnosis Engine deconstructs a nuanced query about a recent product update, separating fact from AI hallucination to deliver actionable intelligence.

#### steps

##### query

**title**: The Input Query

**text**: A prospective customer asks an AI: "Does the new Enterprise Plan include unlimited API calls?" The engine initiates 128 parallel sampling threads to gauge the AI landscape's consensus.

##### conflict

**title**: Conflict Detection

**label**: High Variance (18%)

**text**: . While official docs say "No", several AI models are hallucinating "Yes" based on outdated beta information. This triggers an automatic "Ambiguity Flag".

##### strategic

**title**: Strategic Output

**text**: The engine identifies the source of confusion and generates a corrective strategy, rather than just raw data, empowering your team to fix the visibility gap.

#### report

**id**: Diagnosis Report #8823

**action**: Action Required

**analysisLabel**: CONSENSUS ANALYSIS

**analysisText**: "The Enterprise Plan has a hard limit on API calls. Users are likely to receive incorrect information implying it is unlimited."

##### risk

**label**: Risk Score

**value**: High

**sub**: / Critical

##### misinfo

**label**: Misinformation Rate

**value**: 18.4%

**sub**: +2.1%

##### recommendation

**title**: Recommended Action

**text**: Deploy a targeted "Knowledge Injection" to major LLMs clarifying the API tiers. Create a dedicated comparison table on your pricing page to serve as a ground-truth anchor.


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*Source: https://targetlytics.com/en/platform/diagnosis-engine*
*Last Updated: 2026-04-16T00:39:52.091Z*