Building a RAG Pipeline is Difficult

Building an effective Retrieval-Augmented Generation (RAG) pipeline involves significant complexity, contrary to the initial perception that it is straightforward, especially as enterprises seek scalable and secure solutions. RAG systems typically use a combination of models, including embedding models and generative large language models (LLMs), to manage two major flows: the ingest flow, which involves data extraction, chunking, encoding, and storage; and the query flow, which handles encoding, retrieval, reranking, and generating responses using LLMs. Despite appearing simple, these processes present numerous engineering challenges, such as text extraction from varied file formats, handling non-English languages, and managing data coordination across different databases. The query flow requires sophisticated strategies like semantic and hybrid searches, contextual augmentation, and reranking to ensure high-quality and relevant results. Recent advancements have shown the potential of smaller, specialized models that can outperform larger LLMs in specific tasks, offering faster performance. Vectara offers a comprehensive platform that abstracts these complexities, providing an API to facilitate the creation of RAG applications, underscoring that building a RAG system is an ongoing process demanding continuous investment in systems engineering and adaptation to evolving technologies.