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Course Outline

Introduction to LlamaIndex

  • Understanding LlamaIndex and its role within the LLM ecosystem.
  • Setting up LlamaIndex: environmental configuration and prerequisites.
  • Foundations of indexing custom data.

LlamaIndex in Action

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

Advanced LlamaIndex Features

  • Utilizing 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.
  • Creating a documentation assistant: a practical LLM application example.
  • Debugging and testing LLM applications.

Deployment and Scaling

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

Ethical and Practical Considerations

  • Exploring 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

  • Proficiency in Python programming and foundational knowledge of machine learning concepts.
  • Experience with API integration and application development.
  • Familiarity with natural language processing is advantageous but not mandatory.

Target Audience

  • Software Developers
  • Data Scientists
 42 Hours

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