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Course Outline

Introduction to LlamaIndex

  • Understanding LlamaIndex and its role in the LLM ecosystem
  • Setting up LlamaIndex: environment configuration and prerequisites
  • Fundamentals of indexing custom data

LlamaIndex in Action

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

Advanced LlamaIndex Features

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

Application Development with LlamaIndex

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

Deployment and Scaling

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

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

  • Knowledge of Python programming and fundamental machine learning concepts
  • Experience with API integration and application development
  • Familiarity with natural language processing is advantageous but not mandatory

Target Audience

  • Developers
  • Data scientists
 42 Hours

Number of participants


Price per participant

Provisional Upcoming Courses (Require 5+ participants)

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