RAG & Retrieval
Grounding
Connecting a model's responses to verified, external data sources rather than relying solely on its training data.
In depth
Connecting a model's responses to verified, external data sources rather than relying solely on its training data. Grounding techniques include RAG, tool use, and web search - they reduce hallucinations by giving the model facts to reference instead of generating from memory alone.
Example
In practice, developers reach for Grounding when they need the capability described above as part of an AI feature or workflow.
Go deeper at Developers Digest
Hands-on guides, comparisons, and tutorials that cover RAG & Retrieval.
FAQ
What is Grounding?
Connecting a model's responses to verified, external data sources rather than relying solely on its training data.
Why does Grounding matter for AI developers?
Grounding sits in the RAG & Retrieval part of the AI stack. Understanding it helps you make better decisions when building, debugging, and shipping AI features.
Where can I learn more about Grounding?
Developers Digest publishes tutorials and videos that cover RAG & Retrieval topics including Grounding. Check the blog and YouTube channel for hands-on walkthroughs.
Related terms
RAG & Retrieval
Hallucination
When a model generates confident-sounding information that is factually incorrect or fabricated.
RAG & Retrieval
Hybrid Search
A retrieval strategy that combines keyword-based search (BM25, TF-IDF) with semantic vector search (embeddings) to get the best of both approaches.
RAG & Retrieval
Knowledge Cutoff
The date after which a model has no training data.
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