What is Grounding

How It Works

Grounding connects model predictions or generated text to specific evidence in the source data. For language models, this means linking claims in generated text to retrieved documents or database records. For multimodal models, grounding connects textual descriptions to specific regions, timestamps, or elements in visual or audio content. This enables users to verify outputs and builds trust in AI-generated information.

Technical Details

Implementation approaches include citation generation (linking statements to source documents), visual grounding (bounding boxes for referred objects), temporal grounding (timestamps for described events), and fact verification against knowledge bases. Grounded generation models produce outputs with inline references to source material. Evaluation measures attribution accuracy, source relevance, and faithfulness to cited evidence.

Best Practices

• Always provide source references alongside AI-generated content in production systems
• Implement verification checks that validate claims against cited sources
• Use retrieval-augmented generation to ensure outputs are grounded in available data
• Design UIs that make it easy for users to check grounding sources

Common Pitfalls

• Providing citations that do not actually support the generated claims
• Grounding in outdated or unreliable sources without freshness or quality checks
• Assuming grounded generation is always factual without verification mechanisms
• Not handling cases where no relevant grounding source exists for a query

Advanced Tips

• Implement multimodal grounding that links text outputs to specific frames, regions, or audio segments
• Use grounding scores to filter or rank AI outputs by confidence in their source attribution
• Build feedback loops where users can flag incorrect grounding to improve the system
• Apply fine-grained grounding at the sentence or claim level rather than document level