Walk me through the full RAG pipeline.

Walk me through the full RAG pipeline.medium

Type: scenario
Topic: me-through-the-full-rag-pipeline
Frequency: common
Tags: rag, walk, through, the, full

Answer

(1) Ingestion: load docs, chunk, embed chunks, store vectors with metadata.

Explanation

(1) Ingestion: load docs, chunk, embed chunks, store vectors with metadata. (2) Retrieval: embed user query, ANN search for top-k chunks. (3) Augmentation: inject retrieved chunks into LLM prompt as context. (4) Generation: LLM generates answer grounded in context. Key decisions: chunk size, overlap, embedding model, retrieval top-k, and whether to rerank before generation.

Follow-upWhat tradeoffs did you consider in that implementation?