Building RAG Systems: Optimizing Vector Embeddings with FAISS for LLM Precision
The advent of Large Language Models (LLMs) has revolutionized how we interact with information, offering unprecedented capabilities in understanding and generating human-like text. However, LLMs often suffer from “hallucinations” or provide outdated information, as their knowledge is limited to their training data. This is where Retrieval Augmented Generation (RAG) systems come into play, offering a powerful solution to ground LLMs in real-time, relevant, and accurate data.