RAG Pipeline Architecture

Retrieval-Augmented Generation is the most practical pattern for making LLMs useful in enterprise settings, but building a production RAG pipeline that delivers accurate, cited, low-latency responses requires careful architecture across ingestion, chunking, embedding, retrieval, and generation stages. In this challenge, you will design a complete RAG pipeline on AWS that ingests documents from S3, processes them through a multi-stage pipeline, and serves contextually grounded responses via Amazon Bedrock. The ingestion layer uses S3 event notifications triggering a Step Functions workflow that handles document parsing (PDF, DOCX, HTML) using Lambda with Textract for complex layouts. The chunking strategy uses semantic chunking with overlapping windows — you will design chunk sizes optimized for the embedding model context window and evaluate recursive character splitting versus semantic paragraph detection. Embeddings are generated using Amazon Bedrock Titan Embeddings and stored in Amazon OpenSearch Serverless with vector search capabilities (k-NN plugin with HNSW algorithm). The retrieval layer implements hybrid search combining dense vector similarity (cosine distance) with sparse keyword matching (BM25) using OpenSearch's neural search pipeline, with reciprocal rank fusion for result merging. The generation stage uses Bedrock Claude with a carefully engineered prompt template that includes retrieved context, citation markers, and guardrails for hallucination prevention. The architecture includes a feedback loop where user ratings of response quality feed back into re-ranking model training. Observability covers embedding quality monitoring, retrieval precision metrics, and LLM response quality scoring. This challenge teaches RAG architecture patterns, vector search optimization, and the engineering required to make LLMs reliable in production.