From Words to Vectors: Understanding the Magic of Text EmbeddingTransformers are the backbone of large language models (LLMs) and have revolutionized natural language processing by enabling models to handle vast amounts of text efficiently. Unlike traditional neural networks, transformers use a self-attention mec...Mar 4, 2025 · 7 min readcustomer-identity-access-management.hashnode.dev
RAG: Putting the pieces together (Part - 3)RAG has significant advantage over traditional generative models that rely solely on pre-trained data. RAG systems incorporate a retrieval mechanism that dynamically fetches relevant information to improve the accuracy and reliability of responses. E...Mar 4, 2025 · 4 min readrag-101.hashnode.dev
RAG: Building blocks (Part - 1)Introduction LLM/Inference LLMs are the "brain" of RAG systems, responsible for interpreting input queries, retrieving relevant knowledge, and generating coherent, context-aware responses. Key roles of LLM in a RA system involve Natural Language Unde...Jan 20, 2025 · 3 min readrag-101.hashnode.dev
RAG: The big picture (Part - 2)To understand RAG, we must take a few steps back and start with understanding what is AI. There are many definitions for what constitutes an AI. The one broadly accepted is that any system which matches or exceeds average human capability in a comple...Jan 20, 2025 · 3 min readrag-101.hashnode.dev
From Words to Vectors: Understanding the Magic of Text EmbeddingTransformers are the backbone of large language models (LLMs) and have revolutionized natural language processing by enabling models to handle vast amounts of text efficiently. Unlike traditional neural networks, transformers use a self-attention mec...Nov 21, 2024 · 7 min readtext-embedding.hashnode.dev
