Week 11: Practical LLM Integration & API Development
1. API Integration Fundamentals
- Working with LLM APIs
- OpenAI API structure
- Anthropic Claude API
- Rate limits and costs
- Prompt engineering basics
- System prompts
- Few-shot examples
- Output formatting
- Error handling
- API failures
- Token limits
- Response validation
2. Systematic Prompt Development
- Chain-of-Thought prompting
- Breaking down complex tasks
- Intermediate reasoning steps
- Validation checkpoints
- Synoptic tagging workflows
- Entity extraction
- Relationship identification
- Structured output generation
- Quality assurance
- Output verification
- Consistency checks
- Edge case handling
3. Building Reliable Systems
- Prompt templates
- Version control for prompts
- Template management
- Dynamic content insertion
- Fallback strategies
- Multiple model approaches
- Graceful degradation
- Error recovery
- Testing frameworks
- Unit testing prompts
- Integration testing
- Regression testing
Required Reading
- OpenAI API Documentation
- "Chain-of-Thought Prompting Elicits Reasoning in Large Language Models"
Learning Objectives
- Master LLM API integration
- Develop robust prompt engineering skills
- Build reliable LLM-powered applications
- Implement systematic testing approaches