Module 1: Course Welcome

Welcome to the course! This module will help you:

  • Understand the course structure and learning path
  • Identify the key skills and knowledge areas covered in the course
  • Recognize the intended audience and assess personal readiness for the course

Module 2: Anthropic Overview

By the end of this module, you'll be able to:

  • Explain Anthropic's approach to AI research and development
  • Describe the key principles of AI safety, alignment, and interpretability
  • Identify and differentiate between Anthropic's AI models

Module 3: Anthropic's Models

At the end of this module, you'll be able to:

  • Identify and differentiate between Anthropic's AI models
  • Understand the benchmarks used to measure generative AI models

Module 4: Generative AI

By the end of this module, you'll be able to:

  • Define and explain key terminology in the generative AI field
  • Describe the process of LLM development, including pretraining, SFT, RLHF, and fine-tuning
  • Understand the concepts of inference and latency in AI applications

Module 5: Working with the API

At the end of this module, you'll be able to:

  • Make successful API requests to Claude using best practices
  • Format messages effectively for optimal AI responses
  • Configure and control API parameters like system prompts, max tokens, and stop sequences
  • Work with different input types including streaming, images, and multi-modal requests

Link to Github

Module 5B: Prompting

At the end of this module, you'll be able to:

  • Structure effective prompts that get consistent, high-quality responses from Claude
  • Apply proven prompt engineering techniques through practical demonstrations
  • Evaluate and improve prompts using best practices and troubleshooting methods
  • Identify common prompt patterns and when to use them

Module 6: Tool Use

At the end of this module, you'll be able to:

  • Design effective prompts that help Claude use tools appropriately
  • Structure outputs to ensure reliable tool interactions
  • Implement best practices for tool-based workflows with Claude
  • Understand and work with different agent architectures
  • Successfully guide Claude in computer interactions and tool manipulation

Link to Github

Module 7: Evals

At the end of this module, you'll be able to:

  • Understand different types of AI model evaluations and when to use them
  • Set up effective human evaluation processes for model outputs
  • Implement automated code-based evaluation methods
  • Design model-graded evaluation frameworks
  • Select and use appropriate evaluation tools for your specific needs

Link to Github

Module 8: RAG and Fine-tuning

At the end of this module, you'll be able to:

  • Understand the fundamentals of Retrieval-Augmented Generation (RAG)
  • Implement embeddings effectively for information retrieval
  • Apply best practices for contextual retrieval in RAG systems
  • Optimize RAG performance through proven tips and techniques
  • Design and implement fine-tuning strategies for improved model performance

Module 9: Course Wrapup

At the end of this module, you'll be able to:

  • Synthesize key learnings from across all course sections
  • Identify next steps in your Claude development journey
  • Connect with resources for continued learning and support