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From Basics to Production: Mastering Retrieval-Augmented Generation (RAG) with Large Language Models (LLMs)
A Comprehensive Two-Part Guide for Implementing and Fine-Tuning RAG for Real-World Applications
I had stayed away from Large Language Models (LLMs) for far too long, but recently, I embarked on a journey to dive deep into this fascinating field. My focus has been mainly on understanding Retrieval-Augmented Generation (RAG). This powerful technique combines the strengths of LLMs with retrieval-based models to generate highly relevant and accurate text. After extensive learning and experimentation, I realized that while there are many articles and training videos on RAG, most tend to either cover the basics or provide overly complex production-level solutions without bridging the gap in between.
With this in mind, I decided to write an article that takes you from the basics of RAG to a more advanced, production-ready approach. This article is divided into two parts:
- Part 1: Covers the foundational concepts and basic implementation of RAG. This section is designed to help you understand the core principles often covered in most tutorials.
- Part 2: Dives deep into fine-tuning RAG for a real-world use case. Here, we explore advanced…
