Generative AI Basics Recap: LLMs, Prompts, and RAG
- Generative AI
- Enterprise h2oGPTe
This lesson walks through a recap of the generative AI starter track so far, distinguishing machine learning and deep learning from generative AI's ability to create new content rather than only analyze existing data. It reviews large language models as the foundation of current generative AI, the balance between accuracy, efficiency, and cost when choosing a model, and applications from AI generated visuals to more natural audio experiences. The lesson also recaps prompt engineering techniques such as role playing and prompt chaining, and introduces retrieval augmented generation for more accurate responses.
Key takeaways
- How generative AI differs from machine learning and deep learning by creating new content, not just analyzing data
- Why balancing accuracy, efficiency, and computational cost matters when choosing a large language model
- What prompt engineering techniques like role playing and prompt chaining do to improve an AI's output
- How retrieval augmented generation combines data retrieval with generation for more context aware answers
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