Vector Search and RAG - Balancing Accuracy and Context

Large language models (LLMs) have made significant strides in machine learning and natural language processing, but they face a unique issue called AI hallucinations, where incorrect or false information is generated. This can happen due to lack of context, training data issues, overgeneralization, or design limitations. Retrieval Augmented Generation (RAG) is an advanced approach that aims to enhance the accuracy and reliability of AI models by providing relevant, current information related to a user's question. RAG helps ensure that models can access the newest data, like recent news or research, to give better answers and reduce mistakes. Building a Retrieval Augmented Generation (RAG) system involves several complex steps and decisions, including choosing an embedding model, selecting an index structure, chunking, determining keywords or semantic search, and integrating rerankers. RAG's ability to handle trillions of tokens makes it ideal for handling massive, ever-changing datasets. Combining RAG's precision with the adaptability of long-context models could lead to a powerful synergy. Evaluating large language models (LLMs) can be challenging, but one solution is to have LLMs evaluate each other by generating test cases and measuring the model's performance.