Welcome to the course

Clod Course Overview: Key Concepts

**I. Course Topics Covered**

• Foundations: Background on Anthropic models.
• Access: How to access models through Vertex.
• Prompt Engineering: Focus on prompting, starting with evaluations and progressing to advanced engineering techniques.
• Advanced Capabilities:
• Tool Use: Expanding Clod's functionality via tools.
• Retrieval Augmented Generation (RAG): Providing task-specific context to Clod.
• Model Context Protocol (MCP): Connecting Clod to external services.
• Agents & Workflows:
• Study of Anthropic agents (Clod Code and Computer Use).
• Applying agents and workflows to solve complex problems.

**II. Prerequisites and Requirements**

• Basic understanding of Python.
• A Python environment capable of running notebooks (most code is executed in notebooks).
• A Google Cloud account with access to both Vertex and Anthropic models.

**III. Tips for Success**

• Active Learning: Write code alongside the instructor; passive viewing is insufficient.
• Pacing: Consider speeding up video playback to cover content faster and facilitate easier review of challenging sections.
• Application: Apply learned techniques by expanding or altering the provided notebooks.
• Support: Utilize Claude to seek help or clarify confusing points at any stage.

Takeaways

• The course covers Anthropic models, focusing on Prompt Engineering and advanced capabilities like Tool Use, Retrieval Augmented Generation (RAG), and Model Context Protocol (MCP).
• Key learning objectives include studying Anthropic agents and applying them to solve complex problems through workflows.
• Prerequisites require a basic understanding of Python and access to a Google Cloud account with Vertex and Anthropic models.
• Success depends on active learning, which involves writing code alongside the instructor and applying techniques to provided notebooks.