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When large language models (LLMs) emerged, enterprises quickly brought them into their workflows. They developed LLMs applications using Retrieval-Augmented Generation (RAG), a technique that tapped internal datasets to ensure models provide answers with relevant business context and reduced hallucinations. The approach worked like a charm, leading to the rise of functional chatbots and search products that helped users instantly find the information they needed, be it a specific clause in a policy or questions about an ongoing project.
However, even as RAG continues to thrive across multiple domains, enterprises have run into instances where it fails to deliver the expected results. This is the case of agentic RAG, where a series of AI agents enhance the RAG pipeline. It is still new and can run into occasional issues but it promises to be a game-changer in how LLM-powered applications process and retrieve data to handle complex user queries.
“Agentic RAG… incorporates AI agents into the RAG pipeline to orchestrate its components and perform additional actions beyond simple information retrieval and generation to overcome the limitations of the non-agentic pipeline,” vector database company Weaviate’s technology partner manager Erika Cardenas and ML engineer Leonie Monigatti wrote in a joint blog post describing the potential of agentic RAG.
The problem of ‘vanilla’ RAG
While widely used across use cases, traditional RAG is often impacted due to the inherent nature of how it works.
At the core, a vanilla RAG pipeline consists of two main components—a retriever and a generator. The retriever component uses a vector database and embedding model to take the user query and run a similarity search over the indexed documents to retrieve the most similar documents to the query. Meanwhile, the generator grounds the connected LLM with the retrieved data to generate responses with relevant business context.
The architecture helps organizations deliver fairly accurate answers, but the problem begins when the need is to go beyond one source of knowledge (vector database). Traditional pipelines just can’t ground LLMs with two or more sources, restricting the capabilities of downstream products and keeping them limited to select applications only.
Further, there can also be certain complex cases where the apps built with traditional RAG can suffer fr …