# What is Grounding? Definition & Technical Mechanics

> **Source:** [SPIDITS AI AI Glossary](https://spidits.com/ai-glossary)  
> **Category:** Information Retrieval  
> **Canonical Citation:** [https://spidits.com/ai-glossary/grounding](https://spidits.com/ai-glossary/grounding)

## Definition
Grounding is the process of anchoring an AI model's generated outputs to verifiable real-world facts, external files, or structured databases. It keeps model predictions factual, grounded, and traceably accurate.

## Architectural Mechanics & Deep Dive
Grounding is the process of anchoring AI model outputs in factual, external, and verifiable sources of information to prevent hallucinations. In RAG systems, grounding is achieved by retrieving reference documents and instructing the model to generate responses based strictly on that context, ensuring outputs are auditable and accurate.

## Industry Applications & Real-World Use Cases
Enterprise RAG pipelines, data verification checks, and search engines

## Frequently Asked Questions

### What is the difference between grounding and fine-tuning?
Fine-tuning updates weights to change behavioral style. Grounding passes actual facts directly into the prompt context to keep answers accurate without updating weights.

### How is grounding achieved?
Typically through RAG (Retrieval-Augmented Generation) systems that fetch matching documents and feed them as a context source to the LLM.

## Related Terminology
- [rag](https://spidits.com/ai-glossary/rag)
- [hallucination](https://spidits.com/ai-glossary/hallucination)
- [vector-database](https://spidits.com/ai-glossary/vector-database)

---
*From the SPIDITS AI Knowledge Base (336+ terms indexed). Explore full technical definitions: [https://spidits.com/ai-glossary](https://spidits.com/ai-glossary)*