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Duration 42 hours (6 days)
Course Outline
Introduction to LlamaIndex
- Comprehending LlamaIndex and its role within the context of LLMs
- Setting up LlamaIndex: environment setup and prerequisites
- Fundamentals of indexing custom data
LlamaIndex in Action
- Querying with LlamaIndex: techniques and best practices
- Constructing query and chat engines using LlamaIndex
- Creating intuitive Streamlit interfaces for LLM applications
Advanced Features of LlamaIndex
- Utilizing retrieval-augmented generation (RAG) for improved data retrieval
- Leveraging vector stores for efficient data management
- Designing and implementing LlamaIndex agents
Application Development with LlamaIndex
- Prompt engineering: chain of thought, ReAct, and few-shot prompting
- Developing a documentation assistant: a practical LLM application
- Debugging and testing LLM applications
Deployment and Scaling
- Deploying applications based on LlamaIndex
- 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 advancements in LLM technology
Summary and Next Steps
Requirements
- Proficiency in Python programming and foundational knowledge of machine learning concepts
- Experience with APIs and application development
- Familiarity with natural language processing is advantageous but not mandatory
Audience
- Developers
- Data scientists