RAG & Retrieval
Data Pipeline
A sequence of automated steps that move and transform data from source to destination.
In depth
A sequence of automated steps that move and transform data from source to destination. In AI applications, data pipelines handle document ingestion, chunking, embedding generation, and vector store population for RAG systems. A well-built pipeline keeps your knowledge base current by processing new documents as they arrive, re-embedding when models change, and cleaning stale data.
Example
In practice, developers reach for Data Pipeline 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 Data Pipeline?
A sequence of automated steps that move and transform data from source to destination.
Why does Data Pipeline matter for AI developers?
Data Pipeline 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 Data Pipeline?
Developers Digest publishes tutorials and videos that cover RAG & Retrieval topics including Data Pipeline. Check the blog and YouTube channel for hands-on walkthroughs.
Related terms
RAG & Retrieval
Knowledge Base
A structured repository of information that an AI system can query to answer questions or provide context.
RAG & Retrieval
Retrieval
The process of finding relevant documents, passages, or data from a knowledge base in response to a query.
RAG & Retrieval
Vector Store
A storage system purpose-built for saving, indexing, and querying vector embeddings at scale.
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