AI & ML Academy Best Practices

In our AI & ML Academy Best Practices, we provide curated guidance, proven patterns, and expert recommendations to help you implement AI and ML solutions on Azure effectively. These resources are designed to accelerate your development process, avoid common pitfalls, and maximize the value of your AI investments through real-world implementations and lessons learned from the field.

In a recent project, a customer needed to extract structured data from legal documents to populate a standardized form. The legal documents varied in length and structure, and the customer required consistent and accurate outputs that mapped directly to the expected form schema. The implemented solution leveraged Azure OpenAI to iteratively process document chunks and update the form output dynamically. A key component to successfully extract the correct output was using Structured Outputs to enforce the desired output fields to populate the form. This article outlines best practices derived from this project, with a focus on scalable methods, reliable structure enforcement, and iterative processing of unstructured legal data. These lessons learned can be leveraged for additional scenarios and document types beyond legal documents in various industries including: - Healthcare: patient record management, clinical data extraction - Finance: automated processing of financial statements, regulatory reporting - Insurance: claims processing, policy management - Supply chain logistics: extracting shipment details and tracking information from shipping documents more »
This document outlines a set of best practices to guide users in submitting quota increase requests for Azure OpenAI models. Following these recommendations will help streamline the process, ensure proper documentation, and improve the likelihood of a successful request. more »
This document provides actionable best practices to reduce hallucinations—instances where models generate inaccurate or fabricated information—when using LLMs. We highlight strategies for effective prompt engineering, data grounding, evaluation, and security using Azure AI services (Azure OpenAI Service, Azure AI Foundry, Prompt Flow, and Content Safety). more »
Generative AI (GenAI) is a powerful tool for creating hyper-personalized experiences across various industries. By automating and scaling the generation of highly tailored content, recommendations, and interactions, organizations can enhance user engagement, build brand loyalty, and improve overall satisfaction. This document outlines best practices for using GenAI in hyper-personalization scenarios, with generalized insights derived from provided workflows. more »
Constrained optimization problems come up in various fields and range from scheduling and logistics to financial planning and resource allocation. By leveraging Generative AI (GenAI) to solve these complex decision-making tasks, organizations can become more efficient and productive in their operations. In this article, we will outline best practices for using GenAI in constrained optimization, using a real-world example, an AI-powered college course scheduling solution. We will also explore other applications, such as workforce scheduling and supply chain management more »
Code conversion is the process of translating code from one programming language to another. This is a critical step for organizations modernizing legacy systems, migrating to new technologies, or improving maintainability. However, manual code conversion is labor-intensive, error-prone, and often requires deep expertise in both source and target languages. This document outlines best practices for leveraging Azure OpenAI GPT-4o for code conversion, addressing challenges, and providing solutions for efficient and accurate outcomes. more »
Learn about the architecture and best practices for deploying a chat application with OpenAI in Azure. There are three independent processes, each illustrated separately:Data ingestion pipeline, Real-time web-based chat application, LLMOps pipeline. more »
Generative AI offers significant potential to streamline processes in domains with complex regulatory or clinical documentation. For example, in the context of prior authorization for surgical procedures, automated response generation can help parse detailed guidelines—such as eligibility criteria based on patient age, BMI thresholds, comorbid conditions, and documented behavioral interventions—to produce accurate and consistent outputs. The following document outlines best practices along with recommended architecture and process breakdown approaches to ensure that GenAI-powered responses are accurate, compliant, and reliable. more »