Serverless Retrieval Augmented Generation with AWS SageMaker.

Introduction

Large language models (LLMs) have taken the tech world by storm, capable of generating human-quality text, translating languages, and writing different kinds of creative content. But LLMs also have limitations. They can struggle with factual accuracy and lack access to real-time information. This is where Retrieval Augmented Generation (RAG) comes in.

RAG combines the power of LLMs with external knowledge retrieval, creating a more robust and informative AI experience. This blog post will guide you through getting started with RAG on Google Cloud.

What is Retrieval Augmented Generation (RAG)?

RAG is a powerful AI design pattern that leverages two key components:

1. Large Language Model (LLM): This is the engine that generates text, translates languages, and performs other creative writing tasks. Google Cloud offers various pre-trained LLMs or allows you to bring your own custom model.
2. External Knowledge Retrieval: This component searches for relevant information from various sources like databases, documents, or even the web, to enrich the context for the LLM.

By combining these elements, RAG allows LLMs to:

• Generate more accurate and factual responses.
• Access and process real-time information.
• Support complex conversational…