Introduction to RAG (Retrieval-Augmented Generation)

I’ve been diving into Generative AI lately, and one thing is clear: if you’ve spent any time with LLMs, you’ve probably run into their limitations. You ask an LLM a highly specific question about a new software library, your company's internal documents, or breaking news, and it either politely declines to answer or confidently makes up a complete lie. The LLMs don't know about the recent information because they are limited to knowledge available till the time they were trained. And they don’t know about your company's internal info or documents because they were never trained on them and it is not safe because anyone with access to that model can get your internal information. So, if you want the LLM to answer the questions based on information or data you provide to it without making it available publicly, how will you do it? Well there are plenty of ways by which you can make this possible. The ways are: Fine-tuning: With the fine-tuning you can train the existing AI model on your