Generative AI for Everyone
A beginner-friendly introduction to generative AI, prompting, LLM applications, generative AI projects, responsible AI, and the technology's impact on work and society.
About
Generative AI for Everyone is a beginner-level DeepLearning.AI course taught by Andrew Ng. It explains how generative AI works, what current systems can and cannot do, and how the technology can be applied in everyday work and business. Learners are introduced to prompting, generative AI project lifecycles, large language models, retrieval-augmented generation, fine-tuning, model selection, tool use, AI agents, automation opportunities, and responsible AI.
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Learning Outcomes
- Understand how generative AI and large language models work at a practical level
- Recognize useful applications and limitations of generative AI
- Write more effective prompts for common tasks
- Understand the lifecycle of a generative AI project
- Recognize the roles of RAG, fine-tuning, model selection, tool use, and AI agents
- Identify opportunities for generative AI in professional workflows
- Understand important risks and responsible AI considerations
Skills Covered
Generative AI, AI literacy, Prompt Engineering, Large Language Models, LLM applications, Retrieval-Augmented Generation, AI project planning, Business process automation, Responsible AI, Data ethics
Syllabus
- Introduction to Generative AI
Introduces how generative AI works, common applications, LLM capabilities and limitations, prompting techniques, and optional image-generation concepts.
- Generative AI Projects
Covers generative AI software applications, project lifecycles, costs, retrieval-augmented generation, fine-tuning, model selection, instruction tuning, tool use, and agents.
- Generative AI in Business and Society
Explores workplace applications, task analysis, automation opportunities, AI teams, responsible AI, societal concerns, and the broader impact of generative AI.
Prerequisites
- No prior AI knowledge required
- No coding experience required
Target Audience
AI beginners, Business professionals, Content creators, Marketers, Managers, Developers new to generative AI, Knowledge workers
Best For
- Beginners seeking a practical introduction to generative AI
- Professionals interested in applying AI at work
- Learners preparing for more specialized prompt engineering or LLM courses
- Business leaders evaluating generative AI opportunities
Pros
- No previous AI or coding knowledge required
- Covers both practical usage and underlying concepts
- Introduces prompting as well as technologies beyond prompting
- Includes RAG, fine-tuning, tool use, and AI agents
- Covers business applications and responsible AI
- Taught by Andrew Ng
Cons
- Provides breadth rather than deep technical implementation training
- Does not teach comprehensive LLM programming
- Advanced AI developers may find some introductory material too basic
Course Facts
- Provider:
- DeepLearning.AI
- Instructor:
- Andrew Ng
- Duration:
- Approximately 6 hours
- Level:
- Beginner
- Language:
- English
Certification & Delivery
Certificate Available
Format: Online, Self-paced
Tools you can use with this course
ChatGPT
A conversational AI model developed by OpenAI that excels at answering questions, writing code, and generating creative content.
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Claude
A sophisticated AI assistant known for its large context window, nuanced writing style, and strong reasoning capabilities.
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Gemini
Google's most capable AI model, built from the ground up to be multimodal and highly efficient.
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Perplexity AI
An AI-powered search engine that provides direct, cited answers to user queries in real-time.
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Related Glossary Terms
AI Agent
An AI agent is a software system that uses an AI model to interpret goals, make decisions, use tools, perform actions, and potentially repeat steps in order to complete a task.
AI Hallucination
An AI hallucination occurs when an AI system generates information that appears plausible but is unsupported, incorrect, fabricated, or inconsistent with reliable evidence.
Fine-Tuning
Fine-tuning is the process of further training an existing AI model on additional task-specific or domain-specific data to modify its behavior or capabilities.
Foundation Model
A foundation model is a broadly trained AI model that can support many downstream tasks and can often be adapted through prompting, retrieval, fine-tuning, or additional tools.
Generative AI
Generative AI refers to artificial intelligence systems designed to create new content such as text, images, audio, video, software code, or structured data.
Large Language Model (LLM)
A large language model is an AI model trained on large amounts of text and other data to understand and generate language by predicting and producing sequences of tokens.