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Course Outline

Introduction to LlamaIndex

  • Comprehending LlamaIndex and its function within LLM ecosystems
  • Preparing the environment and prerequisites for LlamaIndex
  • Fundamentals of indexing custom data

LlamaIndex in Practice

  • Techniques and best practices for querying with LlamaIndex
  • Constructing query and chat engines using LlamaIndex
  • Developing user-friendly interfaces for LLM applications via Streamlit

Advanced LlamaIndex Capabilities

  • Utilizing retrieval-augmented generation (RAG) to improve data retrieval
  • Harnessing vector stores for streamlined data management
  • Designing and implementing agents within LlamaIndex

Application Development with LlamaIndex

  • Prompt engineering strategies: chain of thought, ReAct, and few-shot prompting
  • Building a practical documentation assistant: a real-world LLM use case
  • Debugging and testing strategies for LLM applications

Deployment and Scaling

  • Deploying applications built on LlamaIndex
  • Scaling LLM applications for high-performance demands
  • Monitoring and optimizing the performance of LLM applications

Ethical and Practical Considerations

  • Addressing ethical implications in LLM applications
  • Safeguarding privacy and data security with LlamaIndex
  • Anticipating future trends in LLM technology

Summary and Future Pathways

Requirements

  • Familiarity with Python programming and foundational machine learning principles
  • Experience in API usage and application development
  • Background in natural language processing is advantageous but not mandatory

Target Audience

  • Software Developers
  • Data Scientists
 42 Hours

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