Contextual RAG: Context-Preserved Retrieval that Finds What Matters

TL;DR

Prepend context snippets to chunks before indexing and combine dense + sparse retrieval; rerank to cut failed retrievals significantly.

What Is Contextual RAG?

A retrieval technique that preserves broader context within each chunk so similarity search has more clues, combined with BM25 and reranking.

When to Use Contextual RAG

• Long enterprise docs and manuals
• Legal or technical content where paragraphs lose meaning alone
• Customer support over large knowledge bases

Building Contextual RAG in N8N

• Generate short context blurbs per chunk (preprocessing step)
• Index contextualized chunks in vector DB and a BM25 index
• At query: perform hybrid search and apply Reciprocal Rank Fusion
• Rerank top candidates and generate with citations

Strengths & Weaknesses

Strengths: large gains in retrieval success and end-to-end accuracy. Weaknesses: extra preprocessing cost, dual indices, relies on blurb quality.

Metrics to Track

• Retrieval success rate and recall
• Answer F1 with/without contextualization
• Latency impact from hybrid + rerank