Teams evaluating AI search often treat “RAG vs vector search” as a straight either-or decision. It usually is not. Vector search is a retrieval method that finds semantically similar content. Retrieval-Augmented Generation (RAG) is a full answer-generation architecture that can […]
For the last few years, AI progress has often been measured by how well models perform on exams. Benchmarks like: Can a model pass the bar? Can it solve graduate-level science questions? Can it answer coding problems? Can it reason […]
The difference between a chatbot, an AI agent, and a private RAG system comes down to what each one is designed to do. A chatbot holds a conversation. An AI agent takes actions and completes multi-step tasks. A private Retrieval-Augmented […]
Training an AI model involves teaching it patterns from large datasets to generate responses, while grounding with Retrieval-Augmented Generation (RAG) uses external knowledge sources at query time to provide accurate, context-specific answers without retraining the model. What Training an AI […]
A Vector Database stores and retrieves high-dimensional embeddings for semantic search, while a Managed Retrieval-Augmented Generation (RAG) Platform combines vector search with AI-powered answer generation, data management, and user-friendly features to deliver ready-to-use AI solutions. For the build-versus-buy context behind […]
Semantic search finds and ranks relevant existing content by understanding user intent, while generative answering creates new, natural language responses using AI, often synthesizing information from multiple sources dynamically. Generative answering goes a step further by using AI to produce […]
Use a vector database when you need fast, scalable semantic search and have the expertise to build AI layers yourself. Opt for a full Retrieval-Augmented Generation (RAG) pipeline if you want an end-to-end AI solution with integrated search, answer generation, […]
