What belongs in a RAG data pipeline?
A RAG data pipeline is more than an embedding job. It starts with source collection and continues through cleaning, chunking, indexing, retrieval testing, and ongoing review of source quality.
Step 1
Source collection
Collect trusted website pages, documents, or internal knowledge sources. Start with explicit scopes and preserve source URLs or file references.
Step 2
Cleaning and normalization
Remove irrelevant page chrome, duplicated sections, stale copy, and extraction noise. Normalize content into a structure that downstream tools can process consistently.
Step 3
Chunking and embeddings
Split content into retrieval-sized units and generate embeddings only after the source material is good enough to trust. Keep boundaries and metadata attached.
Step 4
Indexing and retrieval testing
Vector search and keyword search both need test queries, source inspection, and feedback loops. Good retrieval is measurable, repeatable, and traceable.
Related reading
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FAQ
Quick answers
What is the first step in a RAG data pipeline?
The first step is selecting trusted source material and collecting it in a reviewable form before cleaning, chunking, or embedding.
Do embeddings solve bad source data?
No. Embeddings can help retrieve content, but they do not fix stale, duplicated, irrelevant, or poorly extracted source material.
Why keep source metadata in a RAG pipeline?
Metadata helps users inspect where retrieved content came from and helps operators debug bad retrieval results.