Short answer
Generative AI is a type of AI that creates new content — text, images, audio, video or code — by learning patterns from very large amounts of data. To learn it, understand at a high level how it works, then practise with large language model assistants: prompting, giving context, asking for structured output, and checking results. After that, explore image and multimodal tools, retrieval over your own documents, and AI agents.
By Chandan Maheshwari, AI mentor & consultant, founder of School of AI · Last updated
Key takeaways
- Generative AI predicts and produces new content from learned patterns.
- Large language models power text-based assistants.
- It is powerful for drafts, analysis and structure, and can be wrong.
- Learn order: concepts → prompting → structured output → RAG → agents.
What generative AI is good at
Drafting, summarising, rewriting, brainstorming, translating, analysing text and data, generating images and code, and structuring messy information.
Its limitations
It can produce confident but false statements (often called hallucinations), may not know recent events, reflects biases in its data, and should not receive sensitive data without checking how that data is handled.
A learning order
Each step builds on the previous one.
- How models work, at a high level: tokens, context, prediction
- Prompting and context
- Structured output: tables, checklists, formats
- Multimodal tools: images, audio, video
- Retrieval over your own documents (RAG)
- Tool use and AI agents
Learning this with School of AI
GenAI Foundation Program
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