Rag

Building a generative AI app in Azure is not only about deploying a model. A useful app needs the right project setup, a suitable model, secure service connections, grounding data, safety controls, and a repeatable evaluation process. This guide turns the AI-102 training material on Microsoft Foundry into a practical build path for teams that want to plan, develop, and evaluate generative AI applications on Azure. Quick Answer To build a generative AI app in Azure with Microsoft Foundry, create a Foundry project, choose and deploy a model from the model catalog, connect to the project with the Microsoft Foundry SDK, build a chat or RAG workflow, add safety controls, and evaluate the app before broad release. ...

Quick Answer Vector Databases and RAG in 2026: Smart Retrieval Architecture Guide helps teams turn RAG and retrieval from a broad AI discussion into a practical decision framework. The useful approach is to define the workflow, identify the data and risk boundaries, choose review controls, and measure whether the system improves real work. Vector databases and retrieval-augmented generation (RAG) help AI systems answer with current, domain-specific information instead of relying only on model memory. A strong RAG implementation has five layers: clean content ingestion, thoughtful chunking, reliable embeddings, hybrid retrieval, and answer evaluation. ...

Quick Answer Enterprise RAG Evaluation Methods for 2026 helps teams turn governance from a broad AI discussion into a practical decision framework. The useful approach is to define the workflow, identify the data and risk boundaries, choose review controls, and measure whether the system improves real work. Retrieval-augmented generation systems are only useful when they retrieve the right context and present answers that users can trust. Key Takeaways Start with the business workflow before choosing a model, vendor, or automation pattern. Separate low-risk experimentation from decisions that affect customers, employees, money, or compliance. Use metadata, review steps, and ownership rules so AI output can be checked and improved. Measure quality, cost, latency, adoption, and exception rates together instead of relying on one metric. Revisit the setup as tools, model capabilities, pricing, and internal policies change. Why It Matters AI adoption becomes expensive when teams copy a demo into production without a repeatable way to evaluate it. Enterprise RAG Evaluation Methods for 2026 gives product, data, security, and operations teams a shared language for deciding what should move forward and what needs more control. ...

Quick Answer Knowledge Base Readiness for AI Assistants helps teams turn RAG and retrieval from a broad AI discussion into a practical decision framework. The useful approach is to define the workflow, identify the data and risk boundaries, choose review controls, and measure whether the system improves real work. AI assistants are only as reliable as the knowledge they can access. Knowledge base readiness means content is current, structured, searchable, and trusted. ...

Quick Answer Vector Database Cost Management for RAG Teams helps teams turn RAG and retrieval from a broad AI discussion into a practical decision framework. The useful approach is to define the workflow, identify the data and risk boundaries, choose review controls, and measure whether the system improves real work. Vector database costs can grow quietly as document collections, embeddings, and retrieval traffic expand. Teams should track storage, index design, query volume, embedding refreshes, and retention rules. ...

Quick Answer RAG Source Quality Scoring for Reliable AI Answers helps teams turn RAG and retrieval from a broad AI discussion into a practical decision framework. The useful approach is to define the workflow, identify the data and risk boundaries, choose review controls, and measure whether the system improves real work. RAG systems depend on the quality of retrieved sources. If the source library is stale, duplicated, conflicting, or poorly structured, even a strong model can produce weak answers. ...