12 posts
Every recorded meeting your organization has ever held is already a knowledge base. It just happens to be stored in the least queryable format imaginable, which...
Why "search" isn't enough anymore A plain RAG pipeline — embed a query, hit a vector index, stuff the top-k chunks into a prompt — works fine for a single-turn ...
Most "RAG tutorials" stop at a single embedding query against a single index. That works for a demo and falls over the moment a real user asks something like "c...
In this article, we will understand how vector search works in Azure AI Search and how to use it as the retrieval layer in a Retrieval-Augmented Generation (RAG...
In this article, we will understand how vector search works in Amazon OpenSearch and how to use it as the retrieval layer in a Retrieval-Augmented Generation (R...
Foundry IQ is Microsoft Foundry's managed knowledge-base layer, built on Azure AI Search under the hood, adding automatic freshness handling and simplified hybr...
Discover how Azure AI Search optimizes enterprise RAG using hybrid retrieval, semantic ranking, and integrated vectorization for production-ready AI apps.
Every RAG tutorial follows the same script: embed your documents, spin up a vector database (Pinecone, Weaviate, pgvector, OpenSearch), manage its infrastructur...
Learn how to build a powerful AI agent using Google Vertex AI Agent Builder, connecting Gemini models to your own data sources for enhanced RAG workflows now.
The transition from "chatting with a PDF" prototypes to production-grade Retrieval-Augmented Generation (RAG) involves a significant shift in architectural complexity. At scale, the challenges shift f...
If you've already shipped a first RAG pipeline in Azure AI Foundry, you've probably hit the point where "it works on the demo doc" stops being good enough. This...
Retrieval-Augmented Generation (RAG) has transitioned from an experimental pattern to the standard architecture for deploying Generative AI in the enterprise. While large language models (LLMs) posses...