RAG & Retrieval
Retrieval
The process of finding relevant documents, passages, or data from a knowledge base in response to a query.
In depth
The process of finding relevant documents, passages, or data from a knowledge base in response to a query. In AI applications, retrieval typically combines embedding-based semantic search with keyword search to find the most relevant context for a model. The quality of retrieval directly determines the quality of RAG systems. Better retrieval means fewer hallucinations and more accurate, grounded responses.
Example
In practice, developers reach for Retrieval 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 Retrieval?
The process of finding relevant documents, passages, or data from a knowledge base in response to a query.
Why does Retrieval matter for AI developers?
Retrieval 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 Retrieval?
Developers Digest publishes tutorials and videos that cover RAG & Retrieval topics including Retrieval. Check the blog and YouTube channel for hands-on walkthroughs.
Related terms
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
RAG (Retrieval Augmented Generation)
A pattern that improves LLM responses by retrieving relevant documents from an external knowledge base and injecting them into the prompt before generation.
RAG & Retrieval
Semantic Search
A search method that finds results based on meaning rather than exact keyword matches.
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