#RAG

AIMostly.) A parser can get every word on the page right and still lose which column a number belongs to. Then your model has to guess. Markdown keeps headings, lists, and tables intact, and it stays readable when you're debugging a bad answer. For tables with merged headers, we switch to HTML. Read our breakdown on why it's our default output for parsing below! ⬇️

AIMicrosoft's Azure AI team argues that better models do not eliminate the need for a dedicated content extraction layer, since agents need trustworthy, structured, and auditable inputs. The post notes that building extraction directly on an LLM quickly demands chunking, layout parsing, grounding, normalization, and evaluation infrastructure. Microsoft positions Azure Document Intelligence and Azure Content Understanding in Foundry Tools as managed options for that layer.

AIGitHub's latest podcast episode examines five common AI hot takes, including whether developers must still read AI-generated code. It argues review effort should match risk, and that Skills and MCP solve different problems. It also says retrieval-augmented generation (RAG) remains useful and works alongside agents, skills, and MCP.

AI…compute! Embeddings are the simplest mechanism that achieve this!