Modular RAG: Can Reasoning Models Replace Traditional Retrieval Pipelines?
kapa.ai experimented with a modular Retrieval Augmented Generation (RAG) system powered by reasoning models to simplify their AI assistant and reduce the need for manual parameter tuning. Using the o3-mini model, they found that while there were modest gains in code generation, the system didn't outperform traditional RAG pipelines in core retrieval tasks like information retrieval quality and knowledge extraction. The experiment revealed a "reasoning ≠ experience" fallacy: reasoning models lack practical experience with retrieval tools and require improved prompting strategies or pre-training to utilize them effectively. The conclusion is that reasoning-based modular RAG isn't currently superior to traditional RAG within reasonable time constraints, but its flexibility and scalability remain attractive.