> ## Documentation Index > Fetch the complete documentation index at: https://notes.kodekloud.com/llms.txt > Use this file to discover all available pages before exploring further. # Module Introduction > Introductory module teaching generative AI fundamentals, model types, agent architectures, and hands-on building of agent-based applications using Microsoft Azure AI Foundry. Welcome to the module Getting Started with Generative AI and Agents. This lesson builds a practical foundation in generative AI: what generative models are, how they differ from traditional AI approaches, and how AI agents combine models, tools, and knowledge to solve real tasks. We start with core concepts, move into the enabling technologies, and finish with hands‑on examples using Microsoft Foundry (Azure AI Foundry). This module is designed to help you progress from conceptual understanding to practical application. Expect concise explanations, clear examples, and step-by-step guidance for beginning to build generative AI applications and agents on Azure AI Foundry. By the end of this module you will be able to: | Learning Objective | What you will understand or do | Example outcome | | - | -: | - | | Explain the fundamentals of generative AI | Distinguish generative models from discriminative/traditional models and know common use cases | Describe how a language model generates text vs. a classifier predicting labels | | Describe types of generative AI models and their capabilities | Identify models (e.g., LLMs, multimodal models), their intended tasks, and typical strengths/limitations | Choose the right model family for text generation, summarization, or multimodal tasks | | Understand AI agents and how they combine models, tools, and knowledge | Explain the agent architecture and how tools/APIs augment model capabilities | Design a simple agent that uses an LLM plus a retrieval tool for question answering | | Start building generative AI applications and agents using Microsoft Foundry | Apply these concepts in an Azure AI Foundry context to prototype solutions | Create a minimal agent-based workflow that integrates a model and an external API | A slide titled "Learning Objectives" showing four numbered goals down the right side. The objectives cover explaining the fundamentals of Generative AI, understanding different generative AI models and AI agents, and getting started building generative AI applications and agents using Azure AI Foundry. Module flow * Conceptual foundation: key terms, model categories (LLMs, diffusion, multimodal), and common generative AI tasks. * Technology primer: how models are trained and served, tool integrations, and agent orchestration patterns. * Practical implementation: guided examples and patterns to build and evaluate generative AI applications and agents on Azure AI Foundry. Recommended next steps * Follow the next lessons to see concrete examples and walkthroughs that connect these concepts to code and Foundry configuration. * Experiment with simple prompts and a basic agent pattern to reinforce the architecture described in the module. Links and references * Microsoft Learn — Azure documentation: [https://learn.microsoft.com/](https://learn.microsoft.com/) * Azure AI (product overview): [https://azure.microsoft.com/en-us/products/ai/](https://azure.microsoft.com/en-us/products/ai/) * Azure AI documentation (services & SDKs): [https://learn.microsoft.com/azure/ai-services/](https://learn.microsoft.com/azure/ai-services/) This documentation is built and hosted on [Mintlify](https://mintlify.com), a developer documentation platform.