🔬 Retrieval-Augmented Generation (RAG) has come a long way from its early days. What started as a simple retrieval-and-generate loop has evolved into a whole spectrum of architectures, each designed to handle growing complexity, scale, and reasoning depth. From Naive RAG, which simply retrieves and generates, to Graph RAG, which reasons over structured relationships, every stage marks a step toward making LLMs more grounded, explainable, and capable of multi-hop reasoning. Check out the full breakdown 👇 As RAG matures, the focus is shifting from better retrieval to better reasoning — bridging the gap between unstructured data and structured understanding. 📅 Want to join live? Register now for the upcoming Agentic AI Bootcamp happening on Nov 25th. Don’t miss your chance to build, test, and evaluate intelligent agents! hubs.la/Q03Rktkq0 #RetrievalAugmentedGeneration #RAG #LLMArchitecture #AIResearch #KnowledgeRetrieval #GraphRAG #ModularRAG #HybridSearch #EnterpriseAI #InformationRetrieval #LLMApplications #AIAgents #KnowledgeGraphs #MachineLearning #ArtificialIntelligence

Think of modular RAG like LEGO. 🧱 You build Retrieval-Augmented Generation systems smarter and faster, as modular RAG treats each part of the process as separate but connected modules. All you have to do is snap them together into place. But how do you do that? Learn everything there is to know about modular RAG by reading our new article 👉 meilisearch.com/blog/modular… #Meilisearch #AI #ModularRAG