Table of Contents
What Problem Does Contextual Retrieval Solve in RAG Systems?
Discover how contextual retrieval solves the biggest flaw in RAG systems: the loss of broader document context when large files are split into smaller chunks.
Question
What problem does contextual retrieval solve in RAG systems?
A. Documents that are too small to be useful
B. Search results that are returned too slowly
C. Chunks losing their connection to the broader document context when split
D. Embedding models that are too expensive to use
Answer
C. Chunks losing their connection to the broader document context when split
Explanation
In traditional RAG systems, large documents are broken into smaller chunks to fit within AI processing limits, which often strips those chunks of their broader meaning (e.g., a chunk might say “revenue grew 3%” but lose the context of which company or year it refers to). Contextual retrieval solves this by using a language model to analyze the whole document and append a brief, orienting summary to each individual chunk before indexing it, ensuring the chunk retains its original context and is retrieved accurately when a user asks a specific question.