Post by @jam.with.ai

RAG stands for Retrieval-Augmented Generation. It helps an LLM answer using external knowledge such as company documents, internal policies, research papers, or private data that the model may not already know. Here is how it works: Documents are split into smaller pieces called chunks. When a user asks a question, the system searches those chunks and retrieves only the most relevant information. That information is added to the prompt, and the LLM uses it to generate the answer. So the basic flow is: Question → Retrieval → Relevant context → LLM → Answer People often ask: why not simply send the entire document, especially now that LLMs support larger context windows? For one or two ad hoc questions, that is completely fine. You can upload a document, ask a question, get the answer, and move on. But if your application repeatedly works with many large documents, sending everything every time becomes inefficient. More text means more input tokens. More input tokens mean higher cost. The model also needs more time to process all that information, which increases latency. And irrelevant information can add noise, making it harder for the model to focus on what actually matters. That is where RAG becomes useful. Instead of sending thousands of pages, RAG retrieves only the few paragraphs needed for the current question. It can also preserve metadata such as the document name, page number, or section, which helps provide citations and evaluate whether the correct information was retrieved. . . . [RAG, Retrieval Augmented Generation, RAG Explained, How RAG Works, RAG Pipeline, Large Language Models, LLM, Context Window, Long Context LLM, Input Tokens, Token Cost, LLM Latency, Document Chunking, Document Retrieval, Knowledge Base, Private Data, Enterprise RAG, Internal Documents, Semantic Search, Vector Search, Hybrid Search, BM25, Embeddings, Vector Database, Reranking, Context Engineering, Grounded Generation, Source Citations, RAG Evaluation, AI Engineering, LLM Engineering, Production RAG, Generative AI] #rag #llm #machinelearning #ai