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Welcome to Generative AI in Practice: Advanced Insights and Operations. I’m Mohsen — your guide through this hands-on exploration of generative AI. This course is built to help professionals move beyond theory and prototypes into production-ready systems that deliver measurable value. If you already understand basic AI concepts and programming paradigms and want to elevate that knowledge into practical impact, you’re in the right place. We’ll assume familiarity with fundamentals so we can focus on advanced, applied content.
This course is designed for engineers, ML practitioners, and technical product owners who want practical, production-focused guidance on generative AI systems, including operationalization, model stewardship, and integration with enterprise data sources.
Below is an illustrative example extracted from a live demo that shows how system context, assistant persona, and runtime logs can appear in a real integration. It demonstrates how prompts, system instructions, and logging interplay during development.
What we’ll cover (high level)
  • The evolution of AI architectures — from rule-based systems and classical ML to modern transformers and multimodal generative models.
  • Deep dive into transformer internals: multi-head self-attention, positional encodings, tokenization, embeddings, and tuning hyperparameters for performance and safety.
  • Responsible AI: bias mitigation, content moderation, privacy preservation, and adversarial robustness with deployment best practices.
  • Operationalization and MLOps for generative systems: model hosting, data ops, monitoring, logging, and continuous evaluation.
  • Advanced Retrieval-Augmented Generation (RAG) and semantic retrieval: pre-retrieval optimization, dynamic embeddings, and post-retrieval re-ranking strategies.
  • Hands-on sessions using production tools, including hands-on examples with Azure AI Studio.
We start by tracing the architectural shifts that made modern generative models possible — focusing on transformers and attention mechanisms.
A presentation slide titled "Transformer Architecture" showing an encoder–decoder diagram with multi-headed self-attention, positional encodings, and output softmax. A small circular video of a presenter appears in the bottom-right corner.
Next, we’ll emphasize Responsible AI practices and concrete techniques you can apply to mitigate harm while maximizing utility.
Responsible AI is not optional. When deploying generative models, implement bias audits, content filters, differential privacy or synthetic-data strategies where appropriate, and continuous monitoring for safety and fairness.
A presentation slide titled "Responsible AI Practices" listing four pillars—Bias Mitigation, Content Moderation, Privacy Preservation, and Adversarial Robustness—with short descriptions under each. A small circular video feed of a speaker appears in the bottom-right corner.
Operationalizing models in enterprise environments is a major part of this course. We’ll demonstrate practical workflows and management UIs using Azure AI Studio for model deployment, health checks, and scaling.
A screenshot of an Azure AI Studio "Manage deployments" page listing model deployments (e.g., gpt-35-turbo, gpt-4) and their statuses. A small circular webcam overlay with a person speaking appears in the bottom-right.
Lifecycle management and data integration are essential for generative systems. We’ll cover data versioning, semantic retrieval pipelines, embedding maintenance, and production-ready monitoring and alerting strategies. A core focus is improving naive RAG pipelines — from pre-retrieval filtering and query augmentation to dynamic embedding strategies and post-retrieval answer synthesis.
A presentation slide titled "Advanced RAG – Addressing Naive RAG's Shortcomings" showing three dark buttons labeled "Pre-Retrieval Optimization," "Dynamic Embedding / Fine-tuning embedding," and "Post-Retrieval Enhancements." A small circular video feed of a presenter appears in the bottom-right.
By mastering advanced RAG techniques and semantic matching, you’ll learn how to integrate dynamic and proprietary data sources into your models, improving contextual accuracy across domains such as finance, healthcare, and customer service.
A man wearing glasses is speaking on camera while a split-screen slide beside him lists a "Generative AI in Practice Curriculum." The slide shows topics like Evolution of AI models, Responsible AI Practices, AI on Azure, and Advanced RAG.
Course module overview Further reading and references Let’s embark on this transformative journey together and unlock the practical power of generative AI.

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