Grounding
Connecting AI model outputs to factual, verified information sources to reduce hallucinations and improve accuracy.
In-Depth Explanation
Grounding in AI refers to the practice of connecting model outputs to factual, verified information sources. This ensures AI responses are based on actual data rather than potentially incorrect training knowledge.
Grounding approaches:
- Document grounding: Using specific documents as source of truth
- Database grounding: Querying structured data
- API grounding: Fetching real-time information
- Web grounding: Searching current web content
- Knowledge graph grounding: Using structured relationships
Benefits of grounding:
- Dramatically reduces hallucinations
- Provides traceable sources
- Enables real-time accuracy
- Works with any LLM
- Maintains control over information
Grounding best practices:
- Provide clear source documents
- Instruct the model to cite sources
- Limit responses to available information
- Include uncertainty when sources are unclear
- Validate claims against sources
Business Context
Grounding is essential for US business AI to ensure responses are accurate, based on your actual data, and compliant with FTC truth-in-advertising standards - not model assumptions.
How Clever Ops Uses This
Every AI solution we build for American businesses includes proper grounding. We connect AI systems to your authoritative data sources, ensuring responses reflect your actual products, policies, and US-specific processes.
Example Use Case
"A customer service bot grounded in your product database gives accurate specs and pricing instead of guessing or making up information."