RAG, Vector Search & AI Architecture

AI Model Training vs. RAG Grounding: Which Is Better?

Some readers search this as “model training vs. RAG,” or as RAG grounding vs. training. These are the same debate. Grounding is what RAG does, it grounds an answer in retrieved source text at the moment the question is asked, rather than relying on what the model memorised during training. This guide uses “fine-tuning” throughout […]

RAG vs Vector Search: Pros, Cons, and When to Use Each

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 use vector search, keyword search, hybrid search, metadata filtering, reranking, and citations to produce a […]

GAIA Benchmark: What It Measures for Enterprise AI

GAIA (General AI Assistants) is a benchmark that tests whether an AI system can actually complete real-world, multi-step tasks, not just answer isolated questions well. It was built by researchers from Meta-FAIR, Hugging Face, AutoGPT, and others, accepted at ICLR 2024, and is now maintained through Hugging Face’s GAIA infrastructure. Here’s why it matters more […]

Chatbot vs AI Agent vs Private RAG: Which Enterprise AI Architecture Is Right for You?

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 Generation (RAG) system answers questions using your own verified knowledge, with citations back to the […]

What Is the Difference Between Using a Vector Database vs. A Managed Rag Platform?

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 managed retrieval systems, see this managed retrieval systems guide. What a Vector Database Does A […]

What’s the Difference Between Semantic Search and Generative Answering for Websites?

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. For a deeper breakdown of where retrieval ends and generated answers begin, see this RAG vs semantic search guide. Generative answering goes a step further by […]

When Should I Use a Vector Database Instead of a Full Rag Pipeline for My AI Application?

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, and user-friendly features. To see where vector databases sit in the larger stack, read the […]