Grounding

What is Grounding?

Grounding in AI refers to connecting a model's outputs to verified external knowledge sources rather than relying solely on information learned during training. The goal is to anchor responses in factual, retrievable information—reducing hallucinations and enabling source verification.

Think of it like the difference between someone speaking from memory versus someone who can check their sources as they speak.

Why Grounding Matters

LLMs trained on internet data encode knowledge in their parameters, but this knowledge:

• Can be outdated (training data has a cutoff)
• May be incorrect (trained on inaccurate sources)
• Lacks citations (can't point to where information came from)
• Degrades under pressure (confident guessing when uncertain)

Grounding addresses these issues by providing external facts as context before generation.

Retrieval-Augmented Generation (RAG)

RAG is the primary technique for grounding AI systems. The process:

1. User submits a query
2. Retrieval system searches a knowledge base for relevant documents
3. Retrieved content is added to the LLM's context
4. LLM generates a response grounded in the retrieved information
5. Sources can be cited alongside the response

This approach ensures the model has access to accurate, up-to-date information and can point to its sources.

Technical Implementation

Embeddings: Documents are converted to numerical vectors (embeddings) that capture semantic meaning.

Vector databases: These embeddings are stored in specialized databases optimized for similarity search.

Retrieval: When a query arrives, it's also embedded, and the most similar documents are retrieved.

Augmented prompting: Retrieved documents are added to the prompt, giving the LLM factual context.

Benefits of Grounding

RAG Variants (2025)

The field has evolved beyond basic RAG:

• Traditional RAG: Standard retrieval + generation
• Self-RAG: Model decides when to retrieve
• Corrective RAG: Validates and corrects retrieved information
• GraphRAG: Uses knowledge graphs for structured retrieval
• Adaptive RAG: Adjusts retrieval strategy based on query complexity

Enterprise Adoption

In 2025, grounding via RAG is essential across industries:

• Customer support: Access to product documentation
• Healthcare: Grounding in medical literature
• Legal: Citation to case law and regulations
• Finance: Real-time market data integration

Limitations

Grounding isn't perfect:

• Retrieval quality: Poor retrieval = poor grounding
• Context limits: LLMs can only process limited context
• Latency: Retrieval adds response time
• Maintenance: Knowledge bases need curation

Related Reading

• Hallucination - The problem grounding addresses
• Confabulation - Hinton's reframing of the issue