9 posts
The Foundry model catalog covers a lot of ground, but it doesn't cover everything. If you need a model trained on your own proprietary, tabular data, a churn pr...
Most "RAG over PDFs" pipelines have a step nobody talks about much: something has to turn a scanned invoice, a multi-column contract, or a photographed receipt ...
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...
Every RAG tutorial follows the same script: embed your documents, spin up a vector database (Pinecone, Weaviate, pgvector, OpenSearch), manage its infrastructur...
If you're building AI agents in 2026, you've probably bumped into at least one of these acronyms: MCP, A2A, AG-UI. Maybe all three. And if you're anything like ...
Explore the architectural shift toward agentic AI as 65% of enterprises prepare for deployment by 2027. Learn about multi-agent systems and infra readiness.
This is the decision that most often gets made by default (whichever tool the first prototype happened to use) rather than deliberately — and it's expensive to ...
Function-calling demos work because the model is well-behaved and the test queries are clean. Production breaks this in three specific ways: malformed arguments...
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...