In the world of AI, particularly in natural language processing (NLP), how a model be prompted can dramatically change its performance. Standard prompting, where a model is simply asked to generate an answer, has been the dominant approach for a long time. But when it comes to tasks requiring reasoning

Retrieval Augmented Generation (RAG) enhances AI responses by retrieving relevant external information in real time. To make this process efficient, RAG relies on chunking strategies - breaking large documents into smaller, manageable pieces for faster retrieval and processing. A great way to understand RAG is to think of it as