Data Engineering
Retrieval Pipeline
The full system that finds, scores, and delivers relevant documents to an AI model, the plumbing behind RAG.
Definition
The end-to-end system for retrieving relevant context for AI models, including query processing, embedding generation, vector search, re-ranking, and context assembly. The quality backbone of RAG systems.
Why it matters
A RAG system is only as good as its retrieval pipeline, brilliant models with bad retrieval give bad answers.
Related terms in Data Engineering
Batch Processing
Processing a large group of data all at once on a schedule, rather than one piece at a time in real-time.
Chunking Strategies
Chopping up long documents into small, bite-sized pieces so an AI can search and read them easily.
Data Augmentation
Creating fake but realistic training examples (like flipping or rotating images) to give the AI more data to learn from.
Data Labeling
The human work of tagging data with correct answers so an AI can learn from it, like marking photos as "cat" or "dog."
From vocabulary to outcomes
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