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A guide to building and deploying resilient RAG applications. #
Introduction #
One of the most in-demand skills in AI engineering is retrieval-augmented generation (RAG). RAG is a technique that improves the responses of LLMs by retrieving relevant information from external data sources before generating answers. Generally, large amounts of information are chunked into documents that are indexed through embedding vectors in what is known as a vector database. When a user submits a query or asks a question, the query can be converted to an embedding vector, and we can use a similarity metric like cosine similarity to find the most similar document vectors and retrieve the most relevant documents.
However, building a production-grade RAG pipeline is not as simple as just throwing a bunch of documents into a vector database, writing a retriever function, and calling it a day. There are additional steps that go into the process of building resilient RAG-based systems, including hybrid search, iterative retrieval, and evaluation with test questions. In this article, I will demonstrate how to build and deploy a production-grade RAG pipeline that answers questions about U.S. federal copyright laws.