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LLM Engineering
Master the art of engineering large language model applications from prompt design to production deployment and optimization.
10 weeks
12 Modules
Certificate Included
Learning Outcomes
What You'll Learn
By the end of this course, you'll be able to:
Design effective LLM pipelines
Implement RAG systems
Build multi-agent frameworks
Optimize LLM performance
Handle LLM limitations
Deploy and scale LLM applications
Curriculum
Course Modules
A comprehensive curriculum designed for practical application.
1
LLM Fundamentals
- How LLMs work
- Model architectures
- Training processes
- Capability assessment
2
Prompt Engineering Deep Dive
- Advanced prompting
- Prompt templates
- Output parsing
- Error handling
3
LLM APIs and SDKs
- OpenAI API
- Anthropic API
- Open-source models
- SDK best practices
4
Retrieval-Augmented Generation
- Vector stores
- Embedding models
- Chunking strategies
- Hybrid retrieval
5
Memory and Context
- Conversation memory
- Summary memory
- Entity tracking
- Long-context handling
6
Tool Use and Plugins
- Function calling
- API integration
- Custom tools
- Tool selection
7
Multi-Agent Systems
- Agent design
- Role-based agents
- Collaboration patterns
- Conflict resolution
8
Evaluation and Testing
- LLM evaluation metrics
- Benchmark datasets
- A/B testing
- Human evaluation
9
Safety and Alignment
- Content filtering
- Jailbreak prevention
- Output validation
- Ethical guidelines
10
Cost Optimization
- Token optimization
- Caching strategies
- Model selection
- Batch processing
11
Production Deployment
- API design
- Scaling
- Monitoring
- Incident response
12
Capstone Project
- Full LLM application
- Integration testing
- Performance optimization
- Documentation
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