Gen AI

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

Reliable RAG is an enhanced Retrieval-Augmented Generation approach that adds multiple validation layers to ensure your AI system gives accurate, relevant, and trustworthy answers. Instead of just retrieving documents and generating responses, Reliable RAG checks document relevance, detects hallucinations, and highlights exactly which sources support each answer. Have you ever asked a RAG-based chatbot a

Multi-Modal RAG is an advanced retrieval system that processes and searches through both text and visual content simultaneously, enabling AI to answer questions using information from documents, images, charts, and diagrams together. This approach significantly improves accuracy when dealing with complex documents that contain visual elements. Think about it – when you read a research

Document Augmentation RAG is an advanced retrieval technique that enhances original documents by automatically generating additional context, summaries, questions, and metadata before indexing them for search. This approach dramatically improves search accuracy by creating multiple entry points and enriched content that matches user queries better. Have you ever searched your document collection and missed relevant

Self RAG (Self-Reflective Retrieval-Augmented Generation) is an advanced AI technique that teaches language models to critique their own performance during the generation process. Instead of blindly retrieving and responding, Self RAG models learn to assess when retrieval is needed, whether retrieved content is relevant, and if their responses are actually supported by the evidence –

Corrective RAG (CRAG) is an advanced RAG technique that adds a quality check layer to your retrieval system. Instead of blindly trusting retrieved documents, CRAG evaluates their relevance and takes corrective action when the retrieved information is poor, irrelevant, or insufficient. I’ll bet you’ve experienced this: you ask a RAG system a perfectly reasonable question,

Explainable Retrieval is a technique that makes RAG (Retrieval-Augmented Generation) systems transparent by showing users which documents were retrieved, why they were chosen, and how confident the system is in its selections. This enables better trust, debugging, and bias detection in AI applications._ You know that situation when an AI gives you an answer but

Hybrid Search-RAG combines vector embeddings for semantic understanding with traditional keyword search for exact matches, giving you the best of both worlds when retrieving relevant documents. Instead of relying on just one search method, hybrid search ranks results from multiple approaches and picks the most relevant ones. Have you ever asked a RAG system about

Semantic Chunking is a context-aware text splitting technique that groups sentences by meaning rather than splitting by fixed sizes. This preserves semantic relationships and dramatically improves retrieval accuracy in RAG systems by ensuring each chunk contains complete, related ideas. If you’ve built a RAG system, you’ve probably noticed something frustrating. Sometimes your AI gives you

HyPE (Hypothetical Prompt Embeddings) is an advanced RAG enhancement technique that precomputes hypothetical questions for each document chunk during indexing rather than generating content at query time. The questions are created in such a way that the answers are already present in the document. By transforming retrieval into question-question matching, HyPE reduces query-time latency while

Optimal chunk size for RAG systems typically ranges from 128-512 tokens, with smaller chunks (128-256 tokens) excelling at precise fact-based queries while larger chunks (256-512 tokens) provide better context for complex reasoning tasks. The key is balancing retrieval precision with context retention based on your specific use case. Ever built a RAG system that gave