RAG Pipeline
The complete end-to-end system for retrieval-augmented generation, including document ingestion, chunking, embedding, indexing, retrieval, reranking, prompt construction, and generation.
Why It Matters
The RAG pipeline is the most common architecture for enterprise AI. Each stage affects the final output quality and needs optimization.
Example
Document upload → chunk into passages → embed with an embedding model → store in Pinecone → retrieve top-K on query → rerank → insert into prompt → generate with LLM.
Think of it like...
Like a factory production line — raw materials (documents) enter one end, pass through processing stages, and a finished product (accurate answer) comes out.
Related Terms
Retrieval-Augmented Generation
A technique that enhances LLM outputs by first retrieving relevant information from external knowledge sources and then using that information as context for generation. RAG combines the power of search with the fluency of language models.
Chunking
The process of breaking large documents into smaller pieces (chunks) before creating embeddings for use in RAG systems. Chunk size and strategy significantly impact retrieval quality.
Embedding
A numerical representation of data (text, images, etc.) as a vector of numbers in a high-dimensional space. Similar items are placed closer together in this space, enabling machines to understand semantic relationships.
Vector Database
A specialized database designed to store, index, and search high-dimensional vector embeddings efficiently. It enables fast similarity searches across millions or billions of vectors.
Reranking
A second-stage ranking process that takes initial search results and reorders them using a more sophisticated model. Reranking improves precision by applying deeper analysis to a smaller candidate set.