The ABC of Retrieval Augmented Generation (RAG) with Implementation— Part 1

Author(s): Shweta

Originally published on Towards AI.

The ABC of Retrieval Augmented Generation (RAG) with Implementation— Part 1

This is a series of multiple articles that will cover the basics of Retrieval Augmented Generation (RAG) and its different building blocks with implementation using LangChain, Accuracy Metrics, Limitations and Challenges. In Part 1, we will try to undertand the building blocks of Traditional RAG and then slowly gravitate to more complex topics in the next set of articles.

The article discusses the limitations of Large Language Models (LLMs) in not being able to provide accurate responses due to outdated data and lack of access to private information, leading to potential inaccuracies or “hallucinations.” It explores the concept of Retrieval Augmented Generation (RAG), which enhances the output of LLMs by utilizing external knowledge bases for more accurate results and addresses the challenges and methods for implementing RAG effectively, including the importance of data retrieval, augmentation, and generation phases.

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Published via Towards AI