What is Grounding in AI?
Connecting AI-generated content to factual sources and real-world knowledge to reduce hallucinations and improve accuracy.
Definition
Grounding in AI refers to the process of connecting AI-generated responses to factual sources, real-world knowledge, or verified information to ensure accuracy and reduce the likelihood of hallucinations or fabricated content.
Purpose
Grounding aims to improve AI reliability by anchoring responses in verifiable facts, citations, and authoritative sources, making AI outputs more trustworthy and accountable for factual claims.
Function
Grounding works by integrating retrieval systems, knowledge bases, or real-time data sources that AI models can reference when generating responses, ensuring claims are backed by credible information.
Example
A grounded AI assistant that answers questions about historical events by citing specific sources, dates, and references, rather than generating plausible-sounding but potentially inaccurate information.
Related
Connected to RAG (Retrieval-Augmented Generation), Knowledge Graphs, Fact-Checking, Citation Systems, and AI Reliability measures.
Want to learn more?
If you're curious to learn more about Grounding, reach out to me on X. I love sharing ideas, answering questions, and discussing curiosities about these topics, so don't hesitate to stop by. See you around!
What is a Backlog?
A backlog is an ordered list of work for a development team that is derived...
What is Disciplined Agile Delivery?
Disciplined Agile Delivery (DAD) is a toolkit that provides a flexible appr...
What is Design Thinking?
Design Thinking is a process for solving problems that focuses on understan...