Grounding

Grounding is a critical concept in AI Agent Quality Assurance that refers to the practice of ensuring every claim, fact, or piece of information provided by an AI agent is supported by verified data sources, documents, or system integrations.

Why Grounding Matters: Without proper grounding, AI agents may generate plausible-sounding but factually incorrect information—a phenomenon known as hallucination. Grounding acts as a fact-checking mechanism that validates AI responses against authoritative sources.

How Grounding Works: 1. Knowledge Base Integration: AI agents are connected to structured knowledge bases containing verified business information, product details, policies, and procedures.

2. Retrieval-Augmented Generation (RAG): Before generating a response, the AI retrieves relevant information from the knowledge base to ensure accuracy.

3. Source Verification: Every factual claim made by the AI is cross-referenced against the available data sources to confirm validity.

4. Real-time Validation: Platforms like Oversai monitor AI responses in real-time, flagging instances where responses cannot be grounded in available sources.

Grounding vs. Hallucination Detection: While hallucination detection identifies when AI provides incorrect information, grounding is the proactive process of preventing hallucinations by ensuring responses are always tied to verified sources.

Oversai's AI Agent QA platform includes advanced grounding checks that verify every AI response against your business ontology, ensuring 100% factual accuracy.