Building Production RAG Pipelines on AWS with Bedrock and OpenSearch

RAG (Retrieval-Augmented Generation) is how enterprises are deploying LLMs without fine-tuning. But most tutorials stop at the demo stage. Production RAG is a different beast entirely. Here's what production RAG actually requires — and how to build it on AWS. RAG vs Fine-Tuning vs Prompt Engineering Approach Cost Data Freshness Accuracy Complexity RAG Medium Real-time High (with good retrieval) Medium Fine-Tuning High Static (retraining needed) High High Prompt Engineering Low Static Variable Low Architecture The pipeline: Documents → Chunking → Embeddings → Vector Store → Query → Retrieval → LLM → Response. Python Implementation import boto3 import json bedrock = boto3 . client ( " bedrock-runtime " , region_name = " us-east-1 " ) opensearch = boto3 . client ( " opensearchserverless " ) def query_knowledge_base ( question : str , collection_id : str ) -> str : # Generate embedding for the question embed_response = bedrock . invoke_model ( modelId = " amazon.titan-embed-text-v2:0 " , b