How even the simplest RAG can empower your team

This article discusses the first part of a series about Retrieval Augmented Generation (RAG) information systems designed to enhance source code development teams. It introduces the concept of a RAG system that can empower development teams by providing deep insights into their codebase without the need to share sensitive information with external providers. Using LangChain, JinaAI embeddings, and Ollama, the article walks through how even a simple RAG setup for Ruby can benefit teams by efficiently indexing and retrieving contextually relevant information. The article additionally highlights the advantages of local LLMs over remote systems like Copilot, emphasizing data control and the ability to customize the information that goes into the RAG knowledge base. It gives insight into how to set up a basic RAG system and test its capabilities with programming libraries such as Discourse. The introduction into embedding models, retrieval mechanisms, and the utility of RAG systems in providing streamlined codebase knowledge helps development teams efficiently tackle coding tasks.

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