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Course Outline

Introduction to LlamaIndex

  • Understanding LlamaIndex and its role in the LLM ecosystem.
  • Setting up LlamaIndex: environment setup and prerequisites.
  • Basics of indexing custom data.

LlamaIndex in Practice

  • Querying with LlamaIndex: techniques and best practices.
  • Building query and chat engines using LlamaIndex.
  • Creating intuitive Streamlit interfaces for LLM applications.

Advanced LlamaIndex Capabilities

  • Implementing retrieval-augmented generation (RAG) for superior data retrieval.
  • Leveraging vector stores for efficient data management.
  • Designing and implementing LlamaIndex agents.

Application Development with LlamaIndex

  • Prompt engineering strategies: chain of thought, ReAct, and few-shot prompting.
  • Developing a documentation assistant: a practical real-world LLM application.
  • Debugging and testing LLM applications.

Deployment and Scaling

  • Deploying applications built on LlamaIndex.
  • Scaling LLM applications for high performance.
  • Monitoring and optimizing LLM application performance.

Ethical and Practical Considerations

  • Navigating ethical implications in LLM applications.
  • Ensuring privacy and data security when using LlamaIndex.
  • Preparing for future advancements in LLM technology.

Summary and Next Steps

Requirements

  • A solid understanding of Python programming and foundational machine learning concepts.
  • Hands-on experience with APIs and application development.
  • Familiarity with natural language processing is advantageous, though not mandatory.

Target Audience

  • Software Developers
  • Data Scientists
 42 Hours

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