The blend of retrieval-augmented generation (RAG) and generative AI models has brought changes to natural language processing by improving the responses to queries. In the realm of Agentic RAG, this conventional method of relying on a monolithic model for tasks has been enhanced by introducing modularity and autonomy. By breaking down the problem-solving process into tools integrated within an agent, Agentic RAG provides benefits like accuracy, transparency, scalability, and debugging capabilities.

The Vision Behind Agentic RAG for Text-to-SQL

Traditional RAG systems often retrieve relevant documents and rely on a single monolithic model to generate responses. Although this is an effective method in some cases, when it comes to structural outputs like the case of generating SQL, this approach may not be the most effective. This is where we can leverage the power of the Agentic RAG framework, where we: