Greeting StackExchanger's, I am a software engineer interested in exploring Retrieval-Augmented Generation (RAG) for my research. However, I am new to this field and do not have practical experience with NLP, NLU, or Deep Learning. I do have some theoretical understanding of deep learning concepts, but I’m finding it challenging to bridge the gap to more advanced topics.
I would appreciate guidance on the following:
What foundational concepts should I focus on before delving into RAG? Are there specific resources (books, courses, blogs, or papers) you’d recommend for beginners looking to understand and implement RAG? What tools and frameworks are commonly used for implementing RAG? Is it still worthwhile to learn and conduct research on RAG in 2025? Additionally, I have reviewed some survey papers on the topic and found them relatively understandable. However, when it comes to papers discussing frameworks, algorithms, indexing methods, and other technical aspects, I find it difficult to follow.
Any advice, resource recommendations, or suggestions for overcoming these challenges would be greatly appreciated.
Thank you in advance for your help!
