
Bridge the gap between technical possibilities and user needs by mastering generative AI foundations, including RAG, agents, fine tuning, and practical AI workflows.
Explore zero-shot prompting and few-shot prompting as prompt engineering techniques, showing how adding examples narrows the model's vocabulary and improves results in tasks like classifying movie reviews.
Understand challenges in implementing rag systems, including data chunking size and meaning, secure indexing, evaluating chunks and answers, keeping data fresh, reducing latency, and managing context limits.
Learn chunking by splitting large text into semantically meaningful chunks, such as questions and answers on a frequently asked questions page, then embed them into a vector store for retrieval.
Learn how fine-tuning tailors a large language model to a specific knowledge base, using prompt engineering or a dataset of questions and answers, with pros, cons, and cost considerations.
Fine-tuning requires extensive data preparation, time, and computing resources, and deploying the model adds hosting costs. Changing data forces restarting the process, risking overfitting and degradation on other subjects.
Explore the ethics of large language models, including misinformation, intellectual property, consent, accountability, and safety guardrails that prevent misuse and guide responsible deployment.
Discover how metrics for evaluating AI systems differ from traditional software metrics and explore using language models as judges to assess AI outputs, including latency considerations.
Discover how AI-assisted testing integrates into the development life cycle by letting AI write tests for new code, run them automatically after each code generation, and fix issues it creates.
Foundations of AI is a beginner-friendly and practical course that helps non-technical professionals understand how modern artificial intelligence works and how to use it confidently in real-world settings. The course is designed for people in roles such as product management, business analysis, project coordination, and other positions that work alongside technical teams. Each concept is explained clearly so that learners gain a strong understanding without needing a background in math or programming.
You will begin by learning what artificial intelligence is and how it differs from traditional software. The course explains the basics of machine learning and deep learning in simple language and shows how these technologies power many tools used today. You will also learn how Large Language Models operate, how they generate text, and why they sometimes produce incorrect information. The course teaches practical prompting techniques that help you get more accurate and consistent results, including structured prompts, examples, and guided reasoning.
Next, you will explore Retrieval Augmented Generation, a technique that allows AI systems to use real-time or organization-specific information. You will learn how chunking, embeddings, and vector stores work together to create reliable and grounded answers. The course also covers common limitations, responsible use, and ways to avoid misleading outputs.
By the end of the course, you will understand the most important AI concepts, communicate more effectively with technical teams, and apply AI tools to improve productivity, research, decision making, and problem solving in your role.