Articles tagged with "RAG"

Large language models (LLMs) can generate complex text and solve numerous tasks such as question-answering, information extraction, and text summarization. However, they may suffer from issues such as information gaps or hallucinations. In this blog article, we will explore how to mitigate these issues using Retrieval Augmented Generation (RAG) and build a low-cost solution in the process.

The way from a cool POC (proof of concept), like a walk in monets garden, to a production-ready application for an RAG (Retrieval Augmented Generation) application with Amazon Bedrock and Amazon Kendra is paved with some work. Let`s get our hands dirty. With streamlit and langchain, you can quickly build a cool POC. This two-part blog is about what comes after that.