Generative AI: Overview and use cases
summary
Survey of methods and demos: GANs produce fast samples but are tricky to train and require a generator/discriminator loop; diffusion models trade speed for higher quality and typically run tens to hundreds of denoising steps. Large language models (Llama 2 at 7/13/70B) use prompt engineering, chain-of-thought and emotional prompting; retrieval augmented generation stores chunked text in a vector store and uses LangChain for lookup. A Streamlit RAG demo and YouTube/PDF chat prototype are built in minutes and work by retrieving passages; a 1M token context window claim enables feeding whole books for complex queries.
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chapters (8)
- 0:00Generative AI overview
- 6:03What is machine learning and generative AI
- 11:06Generative adversarial networks explained
- 17:52Prompt engineering
- 23:09Chatting with RAG
- 35:41Extending LLMs with tools
- 40:16AI agent workflows
- 44:04The future of generative AI and career advice
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