Hallucination Detection

Hallucination in AI models refers to the generation of outputs that are not grounded in the input data or real-world knowledge. This can lead to misleading or incorrect information being presented as factual. For example:

System: Provide Python code to read a CSV file into a pandas DataFrame.

AI:
"""
import pandas as pd

df = pd.readCsv("data.csv")  # Incorrect: pandas has no readCsv; the correct function is read_csv
"""

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Types of Hallucination

1. Factual Hallucination: The AI generates information that is factually incorrect or unsupported by the input data.
2. Contextual Hallucination: The AI provides information that is out of context or irrelevant to the user’s query.
3. Logical Hallucination: The AI’s response contains logical inconsistencies or contradictions.

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Addressing Hallucination on TrueFoundry

TrueFoundry offers solutions to mitigate hallucination through various integrations:

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AWS Bedrock Guardrails

• Monitors AI outputs for factual accuracy.
• Provides context-aware validation.
• Read how to configure AWS Bedrock Guardrails on TrueFoundry here.

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Guardrails AI Integration

• Utilizes trained ML models to detect hallucination patterns.
• Real-time analysis of AI outputs for inconsistencies.

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Azure AI Content Security

• Cross-references AI outputs with verified data sources.
• Enforces custom policies to ensure output reliability.

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Custom Webhook Security

• Allows for custom logic to validate AI-generated content.
• Analyzes output patterns for anomalies and inconsistencies.
• Read how to configure Custom Webhook on TrueFoundry here.

TrueFoundry’s integrations ensure robust detection and mitigation of hallucination, enabling the deployment of reliable AI applications.