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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
 42 Hours

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