June 17, 2025

Graph RAG is an advanced RAG technique that connects text chunks using vector similarity to build knowledge graphs, enabling more comprehensive and contextual answers than traditional RAG systems. Graph RAG understands connections between chunks and can traverse relationships to provide richer, more complete responses. Think about the last time you asked an AI a complex […]

Adaptive RAG is a dynamic approach that automatically chooses the best retrieval strategy based on your question’s complexity – from no retrieval for simple queries to multi-step retrieval for complex questions. Instead of using the same heavy approach for every question, it adapts like a smart assistant who knows when to look things up and

RAPTOR (Recursive Abstractive Processing for Tree-Organized Retrieval) is an advanced RAG technique that creates hierarchical tree structures from your documents, allowing you to retrieve information at different levels of detail and abstraction. Unlike traditional RAG that searches through flat document chunks, RAPTOR builds a multi-level tree where each layer contains increasingly abstract summaries, making it

Fusion RAG is a technique that combines vector and keyword search scores to find more relevant documents for your system. Instead of relying on just one search approach, it normalizes scores from different methods and creates an intelligent weighted average to give you better, more comprehensive answers. Think about it this way: when you’re looking

Feedback Loop RAG is an advanced RAG technique that learns from user interactions to continuously improve retrieval quality over time. Unlike traditional RAG systems that remain static, this approach learns from each interaction to deliver more accurate and personalized responses. Picture this: You build a RAG chatbot for your company. On day one, it gives