Super charge your LLMs with RAG at scale using AWS Glue for Apache Spark

This article discusses how to supercharge large language models (LLMs) with Retrieval Augmented Generation (RAG) at scale using AWS Glue for Apache Spark. RAG helps optimize LLM output by referencing external authoritative knowledge bases before generating responses.

Specifically, the article covers:

• Data preprocessing for RAG, including cleaning, chunking, and embedding documents
• Solution overview using LangChain, AWS Glue, Apache Spark, Amazon SageMaker JumpStart, and Amazon OpenSearch Serverless
• Setting up a vector store with Amazon OpenSearch Serverless
• Preparing documents by parsing HTML, chunking, and embedding
• Demonstrating question-answering using the ingested embeddings and an LLM
• Cleanup of resources