Introduction: Breaking Down the Server-Side Barriers Picture this: You're a JavaScript developer who's mastered frontend development. You know React, you can handle complex DOM manipulations, and you've built stunning user interfaces. But suddenly, y...
In today’s generation, it’s important to learn how to play with AI agents so that you don’t fall behind in this fast-changing AI-driven world. Every day, people are discovering new ways to use AI for writing, coding, research, creativity, and problem...
Artificial Intelligence is powerful, but sometimes it struggles with giving accurate and up-to-date answers. That’s where Retrieval Augmented Generation (RAG) comes in. Let’s break it down in simple words. 🌟 What is RAG? RAG is a technique that hel...
Every day, billions of devices—like computers, smartphones, and smart TVs—talk to each other over the internet. But how do they know where to send and receive data? The answer is Internet Protocol (IP). Just as houses have unique addresses, every dev...
Table of contents Basic RAG flow Why improve RAG Query rewriting / translation CRAG (Corrective RAG) Ranking HyDE Advanced concepts covered: scaling RAGs, speed vs accuracy tradeoffs, LLM-as-evaluator, sub‑query rewriting, ranking strategies,...
If you hand a bright student a book and ask them a question, they’ll flip through the pages, find the right paragraph, and give you an answer. That’s essentially how Retrieval-Augmented Generation (RAG) works. But in reality, things aren’t that simpl...
Introduction Retrieval-Augmented Generation (RAG) has rapidly evolved from a clever workaround for LLM hallucinations into a serious architectural pattern for enterprise AI. At its simplest, RAG retrieves relevant documents and injects them into a la...
Figure: A typical Retrieval-Augmented Generation (RAG) pipeline. The user’s query (1) is used to search for relevant documents (2) in the knowledge base. Those top documents are fetched as enhanced context (3–4) and fed with the original prompt to an...
Large Language Models (LLMs) like ChatGPT, Claude, or Gemini are powerful, but they come with a major limitation: their knowledge is frozen at the point of training. Ask a model trained in 2023 about a 2025 news headline, and it might hallucinate, ma...
We know about the basic RAG structure - retrieve relevant documents, augment them with a query, and generate responses. But there are many flows in it that cause significant problems in real-world applications. Let's first understand what the problem...
