Large Language Models

What You Will Learn

• 1 Learn the foundational concepts of language modeling, including probability, sequences, and how machines process text.
• 2 Understand the transformer architecture that powers modern LLMs, including attention mechanisms, positional encoding, and feed-forward layers.
• 3 Explore the pretraining process, including data collection, tokenization, objective functions, and compute requirements.
• 4 Analyze fine-tuning strategies such as supervised fine-tuning, instruction tuning, and parameter-efficient methods like LoRA and adapters.
• 5 Understand reinforcement learning from human feedback (RLHF) and its role in aligning LLM behavior with human preferences.
• 6 Learn prompt engineering techniques including zero-shot, few-shot, chain-of-thought, and structured prompting patterns.
• 7 Discover retrieval-augmented generation (RAG) architectures and how to ground LLM outputs in external knowledge sources.
• 8 Apply LLM evaluation frameworks including benchmarks, human evaluation, and automated quality metrics.
• 9 Explore LLM safety, bias, and responsible deployment including red-teaming, content filtering, and governance practices.
• 10 Understand LLM deployment infrastructure including serving strategies, latency optimization, and cost management.
• 11 Build LLM-powered applications using agents, tool use, memory systems, and multi-step reasoning pipelines.
• 12 Develop a strategic understanding of the LLM landscape including open-source vs. proprietary models, emerging research directions, and career pathways.