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.

Upcoming Course: Hands-on RAG Systems for Production — Before we dive deeper on this topic…

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01 👇 𝗔𝗜 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝘀 & 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗲𝗿𝘀: For AI engineers/developers looking to master production-ready RAG systems combining search with AI models.

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