Author(s): Amol Mavuduru

Originally published on Towards AI.

A guide to building and deploying resilient RAG applications.

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.

The rest of the article walks through building and deploying a production-grade RAG pipeline for answering questions about U.S. federal copyright law: it outlines core resilience components (hybrid search, iterative retrieval, evaluation, guardrails/security, semantic caching, and fault-tolerant infrastructure), shows how to download and chunk the copyright PDF into documents and embeddings, creates a persistent Chroma vector database, and adds semantic caching for repeated questions. It then defines a hybrid retriever (combining embedding and BM25 with reciprocal rank fusion), builds an iterative retrieval agent that rewrites queries when needed, and wraps it with prompt safety filtering plus cache lookup/update logic. Next, it demonstrates how to generate and review a test set of question/answer pairs using LLMs, evaluate the RAG system with Ragas metrics (context recall, faithfulness, factual correctness), and expose the agent via a FastAPI REST API with retries and health checks. Finally, it covers containerizing with Docker, running locally, and deploying the service on AWS ECS (with notes on an alternative AWS Lambda approach), so the result is an API-backed RAG system that performs and is measurable in production.

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Published via Towards AI