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Course Outline
Introduction to LlamaIndex
- Understanding LlamaIndex and its role in LLMs
- Setting up LlamaIndex: environment and prerequisites
- The basics of indexing custom data
LlamaIndex in Action
- Querying with LlamaIndex: techniques and best practices
- Building query and chat engines with LlamaIndex
- Creating intuitive Streamlit interfaces for LLM applications
Advanced LlamaIndex Features
- Employing retrieval-augmented generation (RAG) for enhanced data retrieval
- Leveraging vectorstores for efficient data management
- Designing and implementing LlamaIndex agents
Application Development with LlamaIndex
- Prompt engineering: chain of thought, ReAct, few-shot prompting
- Developing a documentation helper: a real-world LLM application
- Debugging and testing LLM applications
Deployment and Scaling
- Deploying LlamaIndex-based applications
- Scaling LLM applications for high performance
- Monitoring and optimizing LLM applications
Ethical and Practical Considerations
- Navigating ethical implications in LLM applications
- Ensuring privacy and data security with LlamaIndex
- Preparing for future developments in LLM technology
Summary and Next Steps
Requirements
- An understanding of Python programming and basic machine learning concepts
- Experience with APIs and application development
- Familiarity with natural language processing is beneficial but not required
Audience
- Developers
- Data scientists
42 Hours
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