The field of Natural Language Processing (NLP) has seen significant breakthroughs with the advent of transformer-based models like GPT-3. These language models have the ability to generate human-like text and have found diverse applications such as chatbots, content generation, and translation. However, when it comes to enterprise use cases where specialized and customer-specific information is involved, traditional language models might fall short. Fine-tuning these models with new corpora can be expensive and time-consuming. To address this challenge, we can use one of the techniques called “Retrieval Augmented Generation” (RAG).

In this blog, we will explore how RAG works and demonstrate its effectiveness through a practical example using GPT-3.5 Turbo to respond to a product manual as an additional corpus.