New Research: Contextual Retrieval Improves Accuracy by 49%

Recent research has demonstrated a simple but powerful technique: adding contextual information to chunks before embedding them.

The Problem

Standard RAG systems chunk documents without considering the broader context. A chunk about "the agreement" might lose critical information about which agreement is being discussed.

The Solution

Contextual retrieval prepends each chunk with a brief description of its context—derived from the surrounding document structure.

For example:

"This chunk is from the liability section of a software license agreement"

"This passage discusses authentication in the API documentation"

Results

In controlled experiments, contextual retrieval showed:

49% reduction

in failed retrievals

Improved relevance

for ambiguous queries

Better handling

of documents with similar content

Implementation Notes

The technique requires an additional LLM call per chunk during ingestion, but the retrieval improvements often justify the cost.

We're currently evaluating this approach for future SmartFAQs.ai updates.