LLM Theory. Personal AI Assistant (OpenClaw)

This module provides a comprehensive exploration of Large Language Models (LLMs), encompassing their foundational mechanics, lifecycle, and practical application. It covers core concepts such as tokenization, context windows, and model economics, alongside advanced topics like fine-tuning, RAG, and agentic systems. Learners will understand the immense computational demands of training, the intricacies of the inference process, and strategies for model selection based on cost and task complexity. The module also critically examines AI adoption challenges, including common failure points like hallucination, context overflow, and security vulnerabilities. Emphasizing secure design and precise context engineering, it guides users in leveraging AI as a cognitive offloading system to amplify skills while mitigating significant risks associated with untethered, autonomous AI.