"Contextual Retrieval tackles a fundamental issue in RAG: the loss of context when documents are split into smaller chunks for processing. By adding relevant contextual information to each chunk before it's embedded or indexed, the method preserves critical details that might otherwise be lost. In practical terms, this involves using Anthropic’s Claude model to generate chunk-specific context. For instance, a simple chunk stating, “The company’s revenue grew by 3% over the previous quarter,” becomes contextualized to include additional information such as the specific company and the relevant time period. This enhanced context ensures that retrieval systems can more accurately identify and utilize the correct information."
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