Enhancing Retrieval Accuracy in RAG with Contextual Retrieval
The Limitations of Traditional RAG
Retrieval-Augmented Generation (RAG) has become a cornerstone technique for enhancing large language models (LLMs) with external knowledge. However, traditional RAG systems often struggle with providing accurate and relevant information, especially when dealing with complex, domain-specific queries.
The core issue lies in how traditional RAG retrieves and presents context to the LLM. Typically, documents are split into chunks, embedded, and stored in a vector database. When a query comes in, the system retrieves the most similar chunks based on vector similarity. However, these chunks often lack sufficient context on their own, leading to ambiguous or incorrect responses.
Let's consider an example from financial document retrieval. Imagine a user asks: "What was the revenue growth for ACME Corp. in Q2 2023?" A relevant chunk might contain the sentence: "The company's revenue grew by 3% over the previous quarter." While this information is correct, it lacks crucial context - which company is it referring to? What specific time period?
Without this context, the LLM may struggle to provide an accurate answer, especially if similar statements exist for other companies in the database.
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Contextual Retrieval
To address these limitations, Anthropic has introduced a new technique called contextual retrieval. This approach aims to enrich chunks with additional context before they are embedded and stored, significantly improving retrieval accuracy and LLM performance.
Here's how contextual retrieval works:
Documents are still split into chunks as in traditional RAG.
For each chunk, an LLM (like Claude) is used to generate aโฆ
Curious to learn more?
Join Professor Mehdi and myself for a discussion about this topic below:
What youโll learn๐ค:
๐ How contextual retrieval works
๐ Performance and implementation considerations
๐ Cost considerations and using prompt caching
๐
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