Articles tagged with "RAG"

Retrieval-Augmented Generation (RAG) solutions are an impressive way to talk to one’s data. One of the challenges of RAG solutions is the associated cost, often driven by the vector database. In a previous blog article I presented how to tackle this issue by using Athena with Locality Sensitive Hashing (LSH) as a knowledge database. One the of the main limitations with Athena is the latency and the low number of concurrent queries. In this new blog article, I present a new low-cost serverless solution that makes use of an embedded vector database, SQLite, to achieve a low cost while maintaining high concurrency.

We’ve used a gamified approach to showcase how Retrieval Augmented Generation enables businesses to use Large Language Models in combination with their company data. Based on the popular board game Who-Is-It?, we created a demo.

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.