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Course Outline
Introduction to LlamaIndex
- Exploring LlamaIndex and its function within LLM ecosystems
- Initializing LlamaIndex: setting up the environment and prerequisites
- Fundamentals of indexing proprietary data
LlamaIndex in Practice
- Executing queries with LlamaIndex: strategies and optimal practices
- Constructing query and chat interfaces using LlamaIndex
- Designing user-friendly Streamlit frontends for LLM applications
Advanced LlamaIndex Features
- Implementing retrieval-augmented generation (RAG) for superior data access
- Utilizing vector stores for optimized data management
- Building and deploying LlamaIndex agents
Application Development with LlamaIndex
- Prompt engineering: chain of thought, ReAct, and few-shot techniques
- Building a documentation assistant: a real-world LLM use case
- Debugging and testing LLM application workflows
Deployment and Scalability
- Releasing LlamaIndex-based applications to production
- Scaling LLM applications for optimal performance
- Monitoring and refining LLM application efficiency
Ethical and Operational Considerations
- Addressing ethical implications in LLM deployment
- Maintaining privacy and data security through LlamaIndex
- Preparing for emerging trends in LLM technology
Conclusion and Future Directions
Requirements
- Familiarity with Python programming and foundational machine learning principles
- Practical experience with API design and application development
- Knowledge of natural language processing is an advantage but not mandatory
Target Audience
- Software Developers
- Data Scientists
42 Hours
