Azure AI Foundry (AI Hub) : Prompt Flow, LLMOps & RAG

Explore generative AI jargons like tokens, system and user prompts, and chat completions; learn how unimodal and multimodal models affect input, output, and costs.

Craft precise, detailed prompts to guide generative AI like Copilot and ChatGPT, incorporating the four parts—goal, context, expectations, and source—for accurate, focused results.

Conduct a hands-on lab to build a named entity recognition prompt flow with an llm component and python data-cleansing. Deploy GPT 4.1, define inputs, run the flow, and view outputs.

Query PromFlow's real-time endpoint via rest calls, sending a JSON body with entity-type and user-query; compare Paris and France accuracy to Azure Language Service.

Explore multimodal retrieval augmented generation with Azure AI Search, combining text, images, and structured documents, preserving layout and citations through image verbalization and layout detection.

Create a rag prompt flow in Azure ai foundry by connecting the ai search index, configuring the index lookup with semantic search and Azure OpenAI embeddings, then generate responses.

Discover how function calling enables GPT models to fetch real-time data by calling external APIs within prompt flows, and define functions with a name, description, and required parameters.

Demonstrate integrating function calling in Microsoft Prompt Flow to fetch air pollutant concentrations via a weather API, convert results to a summary, and determine London's air quality index.

Launch and test a computer vision resource with Vision Studio, connect it, and call the api endpoint using the primary key to generate dense captions and detect objects.

Build a multimodal image analysis prompt flow in Azure AI Studio that inputs a user query and image URL, analyzes the image with Python, and returns an LLM response.

Explore a PII protection prompt flow for healthcare using Azure language service to redact personally identifiable information in LLM outputs with redacted text via named entity recognition.

Explore the fundamentals of responsible AI, including fairness, privacy and security, inclusiveness, transparency, and accountability, plus a four-stage process to identify, measure, mitigate harms, and operate AI workloads.