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Intro to AI-Centric EvaluationAI Bootcamp- Metrics and Evaluation Design - Foundation for Future Metrics Work - Building synthetic data for AI applications
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From Theory to Practice — Building Your First LLM ApplicationAI Bootcamp- Understand how inference works in LLMs (prompt processing vs. autoregressive decoding) - Explore real-world AI applications: RAG, vertical models, agents, multimodal tools - Learn the five phases of the model lifecycle: pretraining to RLHF to evaluation - Compare architecture types: generic LLMs vs. ChatGPT vs. domain-specialized models - Work with tools like Hugging Face, Modal, and vector databases - Build a “Hello World” LLM inference API using OPT-125m on Modal
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Navigating the Landscape of LLM Projects & ModalitiesAI Bootcamp- Compare transformer-based LLMs vs diffusion models and their use cases - Understand the "lego blocks" of LLM-based systems: prompts, embeddings, generation, inference - Explore core LLM application types: RAG, vertical models, agents, and multimodal apps - Learn how LLMs are being used in different roles and industries (e.g., healthcare, finance, legal) - Discuss practical project scoping: what to build vs outsource, how to identify viable ideas - Identify limitations of LLMs: hallucinations, lack of reasoning, sensitivity to prompts - Highlight real-world startup examples (e.g., AutoShorts, HeadshotPro) and venture-backed tools
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