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 does “training” an […]
RAG systems combine vector search with AI-generated answers, offering rich, context-aware responses but require more complex setup. Vector-based search engines excel at fast, scalable semantic retrieval but lack built-in generative capabilities. What are vector-based search engines? They store embeddings of […]
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. What does a Vector Database […]
A chatbot typically handles scripted conversations, an AI agent uses advanced natural language understanding to perform dynamic tasks, and a private Retrieval-Augmented Generation (RAG) system combines AI with private knowledge bases for context-rich, secure responses. Choosing the right solution depends […]
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, […]
