RAG, Search, and Long Context: Why Complex Pipelines Aren't Always Necessary

This article analyzes the evolution of RAG (Retrieval-Augmented Generation) systems in the context of modern LLM development. The author notes that in 2023, RAG was a necessary tool for working with knowledge bases due to the limited context windows of models. However, today, with many models supporting context windows of 1 million tokens or more, the need for complex and resource-intensive RAG pipelines (chunking, vector databases, rerankers) for smaller datasets is becoming questionable. The article offers a critical look at the choice between using long context and the classic RAG approach. The author examines when building a complex architecture is justified and when it is sufficient to load documentation directly into the model. This material helps developers optimize resources and choose the most effective data-handling tools, avoiding over-engineering where modern LLM capabilities allow for simpler solutions.