Implement information extraction solutions

A user asks a chatbot, 'which of our supplier contracts renew next quarter?' The contracts are a folder of scanned PDFs, so nothing about that question is answerable until two different jobs run in sequence. First something has to read each file and hand back clean, structured text; then something has to find the few passages that bear on renewals and put them in front of the model. That is the whole domain in one line: get the content out of the file, then get the right content in front of the model. Those two jobs are the domain's two subtopics, and they meet at a single seam, clean structured text (often Markdown). The trap this model helps you dodge is treating the model at the end as if it could do the earlier jobs itself. A chat model cannot reliably read a scanned page, cannot rank a large corpus, and is not an authorization boundary, so pasting a raw PDF, or the whole library, into a prompt skips both stages and fails. Filing a concern on the wrong side of the seam is the quieter version of the same mistake: optical character recognition (OCR) and confidence scores live in extraction, while chunking and vectors live in retrieval.

Walk the two subtopics in the order the data flows. Document content extraction with Content Understanding owns the first stage: Content Understanding, the umbrella content-AI service that pairs deterministic Document Intelligence extraction with large language model (LLM) analyzers, reads a file and returns faithful structured text, covering OCR, tables and layout, prebuilt versus custom analyzers, standard versus pro processing mode, and opt-in confidence and grounding. Reach for it whenever the input is a file and what you need is clean structured output; its job ends the moment the document is clean text. Retrieval and grounding pipelines with Azure AI Search owns the second stage: an ingestion pipeline chunks that text and turns each chunk into a vector written to an Azure AI Search index, and at query time a retrieval query returns the most relevant chunks to hand the model as grounding for retrieval-augmented generation (RAG). Reach for it once you already hold content and need the right slice of it in front of the model. The seam between them is deliberate: turning clean text into a searchable index, with chunking and vectors, is retrieval's job, not extraction's.

Across both stages the exam rewards the same instinct: let the managed, purpose-built component carry the work, and never make the model do a job a dedicated service already does. In extraction, start from a prebuilt analyzer, which needs no model deployment and carries no generative token cost, before authoring a custom one, and invoke an analyzer as an agent tool rather than pasting raw OCR text into the prompt. In retrieval, the documented default is hybrid search with the semantic reranker rather than vector similarity alone, per-user access is enforced by a query filter and never by a system-prompt instruction, and the retriever is judged against human relevance labels, not against how good the final answer happens to read. The through-line is one idea: the model consumes clean, pre-filtered, grounded content; it is not the component that reads the file, ranks the corpus, or checks permissions.