Diagnosis Engine: The Core of Visibility
Deep dive into our N-Sampling & Consistency architecture. Understand how we mitigate AI hallucinations through rigorous statistical analysis and multi-model verification.
N-Sampling Architecture
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.
Consistency & Variance Checks
We measure the semantic distance between N outputs. A lower variance indicates high confidence and factuality, while high variance suggests hallucination or ambiguity.
Performance Metrics
Scenario Analysis: Brand Reputation
See how the Diagnosis Engine deconstructs a nuanced query about a recent product update, separating fact from AI hallucination to deliver actionable intelligence.
The Input Query
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 Detection
High Variance (18%). While official docs say "No", several AI models are hallucinating "Yes" based on outdated beta information. This triggers an automatic "Ambiguity Flag".
Strategic Output
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.
Live Diagnosis Output
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.
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.
{
"model_diagnosis_result": {
"id": "diag_8823_jd92",
"timestamp": "2024-12-27T14:30:00Z",
"input_hash": "a1b2c3d4...",
"metrics": {
"n_samples": 128,
"consistency_score": 0.998,
"variance_detected": false
},
"condensed_output": "The brand sentiment for Q3 is positive due to...",
"deviations": [
{
"sample_id": 42,
"deviation": "Sentiment negative due to unrelated weather events...",
"weight": 0.002
}
]
}
}Aggregated Response
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.
Confidence Score
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.
Hallucination Handling
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.