Introduction to AI Concepts: From GenAI to Responsible AI

About this course

Artificial intelligence is now part of almost every software project but the terminology moves fast, and it is easy to get lost between "LLMs", "agents", "embeddings", "OCR" and "responsible AI". This course gives developers and technology professionals a clear, practical map of the AI landscape, so you can recognise which kind of AI fits a problem and talk about it confidently with your team.

Each lesson explains one AI workload in plain language, with real-world examples, simple visual explanations and a short recap. No heavy math, no hype just the concepts you need to design, evaluate and discuss AI solutions.

What we cover:

• What AI is, and the main categories of AI workloads
• Generative AI: prompts, tokens, context, grounding (RAG) and how AI agents plan and use tools
• Text and natural language: sentiment, key phrases, entities, summarisation, translation and question answering
• Speech: speech-to-text, text-to-speech, translation, and real-time vs batch scenarios
• Computer vision: classification, object detection, captioning and OCR
• Information extraction: turning documents and forms into structured data
• Responsible AI: fairness, reliability, privacy, security, transparency and accountability
• A guided walkthrough of realistic AI workloads that connects every concept
• A summary framework you can reuse to choose the right AI approach

Examples reference Microsoft Azure AI services where helpful, so the course is also a friendly starting point if you are exploring Azure AI Fundamentals topics but the concepts apply to any platform.

By the end, you will be able to look at a business problem and say: which AI workload this is, what the inputs and outputs are, what the risks are, and what to evaluate before shipping.

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