Designing Agentic AI Systems, Part 4: Data Retrieval and Agentic RAG

Agentic systems, which are designed to perform tasks using language models, often require data retrieval techniques to enhance their functionality, with Retrieval Augmented Generation (RAG) emerging as a standard approach. RAG involves retrieving additional context from external knowledge bases, augmenting user input with this context, and then generating responses using the language model's pre-trained knowledge. Structured data and APIs facilitate the integration of agentic systems in industries like insurance, where mature API programs simplify the implementation process. Unstructured data, however, presents challenges due to its variability and lack of schema, necessitating the use of vector databases and semantic search techniques for effective retrieval. RAG pipelines transform unstructured data into optimized search indexes, enabling language models to access relevant information efficiently. These pipelines must be adept at handling real-time updates to maintain data accuracy and reliability. The complexity of working with unstructured data and vector indexes poses unique challenges for developers accustomed to traditional data pipelines, highlighting the need for innovative solutions to ensure high-quality outputs from agentic systems.