@Apurvak3

📊 How to Evaluate RAG (Retrieval-Augmented Generation) Systems — Complete Guide

2d ago · 4 min read · Building a RAG system is only half the job. The real challenge is evaluating whether your system is actually working correctly. Unlike traditional ML systems, RAG has two critical components: Retriev

🚀 How RAG (Retrieval-Augmented Generation) Works — Step-by-Step Guide

2d ago · 4 min read · Retrieval-Augmented Generation (RAG) is one of the most powerful techniques used in modern AI systems to make Large Language Models (LLMs) more accurate, up-to-date, and context-aware. Instead of rely

FlashAttention: Making Transformers Faster and More Memory-Efficient

Dec 26, 2025 · 5 min read · Large Language Models (LLMs) like GPT, BERT, and modern Transformers rely heavily on the self-attention mechanism. While powerful, self-attention is also the biggest performance bottleneck when working with long sequences. In 2022, Tri Dao and collab...

🧠 Transformers Explained Simply: From Word2Vec to Multi-Head Attention

Nov 2, 2025 · 5 min read · # A deep dive into the paper “Attention Is All You Need” and how modern NLP models like BERT and GPT evolved from it. 🌟 Introduction If you’ve ever wondered how models like BERT, GPT, or T5 understand language, the answer lies in one architectur...

# 🧠 Neural Machine Translation of Rare Words with Subword Units —

Oct 29, 2025 · 4 min read · By Apurva KanthPublished on Hashnode 💭 The Problem: When Neural Networks Don’t Know a Word Traditional Neural Machine Translation (NMT) systems work with a fixed vocabulary — often limited to around 30,000–50,000 words. But languages are full of ra...