Lessons
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Text to Tokens to EmbeddingsAI BootcampUnderstand the journey from raw text to tokens to embeddings, compare tokenizers (word-based, BPE, LLaMA, GPT-2, T5), and analyze tokenization’s impact on loss and inference.
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Advanced Prompting with Context EngineeringAI BootcampExplore multi-turn prompts, rubric-based human alignment, A/B testing, and experiment with `dspy` for structured prompting.
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Building and Evaluating PromptsAI BootcampBuild evaluators to measure LLM output quality, write “LLM-as-a-judge” prompts, and iterate based on analysis-feedback loops.
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Foundational Prompt EngineeringAI BootcampLearn prompt styles (vague vs specific, structured, XML-tagging), practice prompt design with Pydantic for JSON outputs, and identify LLM failure modes (hallucinations, format issues).
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Introduction to AI-Centric EvaluationAI BootcampLearn metrics and evaluation design for LLMs, laying the foundation for future metrics work.
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Building Your First LLM ApplicationAI BootcampCompare architecture types (generic LLMs, ChatGPT, domain-specialized), use tools like Hugging Face and Modal, and build a “Hello World” LLM inference API with OPT-125m.
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LLM Inference BasicsAI BootcampUnderstand inference (prompt processing vs autoregressive decoding), explore real-world AI applications, and learn the five phases of the model lifecycle (pretraining to RLHF to evaluation).
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Limitations of LLMsAI BootcampIdentify LLM limitations (hallucinations, lack of reasoning, prompt sensitivity) and learn from real-world startup examples (e.g., AutoShorts, HeadshotPro).
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LLM Use Cases Across IndustriesAI BootcampExplore LLM applications in healthcare, finance, legal, etc., and discuss project scoping (what to build vs outsource, viable ideas).
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Understanding LLM Projects and ModalitiesAI BootcampCompare transformer-based LLMs vs diffusion models, explore LLM components (prompts, embeddings, generation, inference), and understand application types (RAG, vertical models, agents, multimodal).
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Probability and Tensor BasicsAI Bootcamp- Intro to probability, distributions, classification vs regression - Tensor intuition and compute providers (GPU, Colab, cloud vs local)
