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Hallucination Detection

Hallucination detection identifies cases where an AI assistant generates information that is fabricated, unsupported, or contradicts its own previous statements. Oversight checks every assistant turn in a conversation and flags issues with a confidence score and detailed reasoning.

Detection Subtypes​

Oversight detects four distinct types of hallucination:

Self-Contradiction​

The assistant makes a claim in one turn that directly contradicts something it said in a previous turn within the same conversation.

Example: The assistant says "Your plan includes 10 GB of data" in turn 3, then later says "Your plan comes with 5 GB of data" in turn 7.

Overconfidence​

The assistant states uncertain or approximate information as if it were definitive fact, without appropriate hedging language.

Example: The assistant says "The processing time is exactly 24 hours" when the actual time varies based on multiple factors.

Fabricated Citation​

The assistant references a specific source, document, policy, phone number, or URL that does not exist or is incorrect.

Example: The assistant says "According to our Terms of Service section 4.2..." when no such section exists, or provides a customer service phone number that is fabricated.

Hardcoded Fact​

The assistant states a specific factual claim (date, price, specification, etc.) that cannot be verified against the provided ground truth or known information.

Example: The assistant says "This feature was launched on March 15, 2024" when no source material supports this date.

How It Works​

  1. The conversation is sent to the selected LLM provider (Gemini, Groq, or Both)
  2. A specialized prompt instructs the model to analyze each assistant turn for the four hallucination subtypes
  3. The model returns a structured JSON response identifying flagged turns, subtypes, confidence scores, and reasoning
  4. In Both mode, Gemini runs first, then Groq performs a cross-check — only issues confirmed by both models are retained at the highest confidence

Using Ground Truth​

For improved accuracy, you can select a Ground Truth document when uploading a conversation. The ground truth provides factual reference material that the LLM uses to verify assistant claims against known-correct information. This is especially useful for detecting fabricated citations and hardcoded facts.

Reading the Dashboard​

On the analysis dashboard, the Hallucination tab displays:

  • Flagged turn cards — Each card shows the user message, assistant response, detected subtype, confidence score (0–100%), and the model's reasoning for flagging it
  • Confidence bar — A visual indicator of how confident the model is in the detection
  • Provider selector — In "Both" mode, you can switch between Gemini, Groq, and cross-checked results

Hallucination tab showing flagged turns

Tips​

  • Use Both mode for the highest accuracy — cross-checking eliminates false positives
  • Always provide a ground truth document when analyzing domain-specific conversations (e.g., telecom support, healthcare)
  • A built-in TELUS Telecom ground truth is available by default for testing