Author: A B Vijay Kumar

In the ever-evolving world of AI and Natural Language Processing (NLP), Large Language Models and Generative AI have become powerful tools for various applications. Achieving the desired results from these models involves different approaches that can be broadly classified into three categories: Prompt Engineering, Fine-Tuning, and Creating a new model. As we progress from one level to another, the requirements in terms of resources and costs increase significantly.

In this blog post, we’ll explore these approaches and focus on an efficient technique known as Parameter Efficient Fine-Tuning (PEFT) that allows us to fine-tune models with minimal infrastructure while maintaining high performance.

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.