Query Rewriting on AI VOID

Intelligent Querying: Leveraging LLMs for Query Rewriting and Multi-Hop Retrieval

Fri, 20 Mar 2026 00:00:00 +0000

Introduction: Beyond Simple Search

Welcome back, fellow RAG enthusiasts! In our previous chapters, we laid the groundwork for Retrieval-Augmented Generation, exploring how to get relevant information to Large Language Models (LLMs) to improve their outputs. We’ve seen how crucial effective retrieval is, but what happens when a user’s question isn’t straightforward? What if the query is ambiguous, uses different terminology than your knowledge base, or requires piecing together information from multiple, distinct sources?

Modern RAG 2.0: Advanced Retrieval Guide

Fri, 20 Mar 2026 00:00:00 +0000

This comprehensive guide delves into the evolution of Retrieval-Augmented Generation, moving beyond basic RAG to explore advanced RAG 2.0 architectures. We cover critical components like hybrid search, vector embeddings, GraphRAG, multi-hop retrieval, and intelligent context assembly. Discover how these modern systems significantly enhance accuracy and relevance, complete with real-world applications and project insights.

RAG 2.0: From Basic to Advanced Retrieval-Augmented Generation

Fri, 20 Mar 2026 00:00:00 +0000

Welcome to Modern RAG: Building Intelligent AI Systems

Hello there! If you’re working with Large Language Models (LLMs), you’ve likely encountered Retrieval-Augmented Generation (RAG). It’s a powerful technique that helps LLMs provide more accurate and up-to-date answers by giving them access to external knowledge. But as you might have noticed, basic RAG can sometimes fall short, especially with complex questions or when dealing with vast, interconnected information.

That’s where RAG 2.0 comes in. Think of it as an evolution, moving beyond simple document retrieval to a more intelligent, adaptive, and highly accurate way of preparing context for your LLMs. This guide will walk you through the essential techniques and best practices to build RAG systems that truly understand and respond to intricate queries.