
Building a PDF-Based RAG Workflow: Step-by-Step Guide
Although current pre-trained large language models (LLMs) have demonstrated strong generalizability across various tasks, they often underperform downstream natural language processing (NLP) tasks due to the lack of domain-specific knowledge. Retrieval-augmented generation (RAG) [1] emerges to address this challenge by retrieving relevant data from a knowledge base to augment the input prompts of LLMs, thereby enhancing their performance on specific tasks.