Fixing Context Loss in RAG Systems

In a world where rapid advancements in AI reshape the technological landscape, the challenge of context preservation in retrieval-augmented generation (RAG) systems stands out. In the video “Can This FIX Context Loss in RAG?” released by Prompt Engineering on August 27, 2025, various cutting-edge methods are explored to address this issue, focusing primarily on contextualized chunk embeddings. Typically, in RAG systems, dividing documents into chunks results in the loss of either local or global context. The video explores notable attempts to overcome these limitations by pre-processing chunks with an LLM to generate contextual retrievals, though being highly efficient, remains impractical for large documents due to cost constraints. An innovative remedy is provided by the approach of embedding level chunking, where chunking occurs after embedding, allowing for both local and global contexts to be preserved. This method employs models with large token capability, producing document-wide token embeddings that are later pooled for chunked embeddings.

The detailed examination of the latent interaction method, which leverages BERT models to compute similarity at a token level rather than a single vector level, reveals promising retrieval improvements but at a computational cost. It’s illuminating how the presentation balances the benefits of these approaches against their resource demands, a common trade-off in machine learning applications.

The host explains that contextualized chunk embeddings aim to preserve context when querying within a RAG system. Unlike late chunking, which delays chunk creation until the embedding process is complete, contextualized chunk embeddings utilize embedding models trained to maintain a document’s contextual integrity, potentially offering superior accuracy. Promisingly, these approaches may yield high retrieval accuracy with fewer computational resources.

The channel then offers a thorough demonstration, illustrating how contextualized embeddings provide more precise retrieval of relevant chunks. The comparisons drawn between different embeddings show a consistent edge in performance for contextualized methods, especially in complex datasets. The video openly acknowledges the challenges, such as balancing chunk sizes with storage efficiency and discussing diverse quantization techniques to optimize performance further.

This exploration of contextualized chunk embeddings by Prompt Engineering not only highlights a breakthrough in addressing context loss but also emphasizes the need for adaptability in technological applications. As the presenter notes, while there isn’t a universal solution to all RAG challenges, understanding and deploying these innovative techniques offers valuable advantages in the evolving AI landscape.