Course Description:
This course teaches advanced prompting strategies and practical model customization to improve reasoning, robustness, and domain fit. Learners will apply structured prompting methods and fine-tuning workflows to adapt models for real-world tasks.

Lecture 1: Advanced Prompting Methods
Explore structured prompting techniques that guide models through stepwise, branching, and networked reasoning. Learn when to use Chain of Thought, Tree of Thought, and Graph of Thought for complex problem solving.

Key Objectives:

• Chain of Thought (COT): Step-by-step reasoning
• Tree of Thought (TOT): Branching exploration
• Graph of Thought (GOT): Networked reasoning

Lecture 2: Model Customization
Cover the fundamentals of adapting models to your use case, from basic fine-tuning to supervised pipelines. Develop domain-specialization strategies that improve accuracy and alignment for specific tasks.

Key Objectives:

• Fine-tuning fundamentals
• Supervised Fine-Tuning (SFT)
• Domain specialization strategies