Course Outline

Introduction to LlamaIndex

  • Understanding LlamaIndex and its role in LLMs
  • Setting up LlamaIndex: environment and prerequisites
  • The basics of indexing custom data

LlamaIndex in Action

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

Advanced LlamaIndex Features

  • Employing retrieval-augmented generation (RAG) for enhanced data retrieval
  • Leveraging vectorstores for efficient data management
  • Designing and implementing LlamaIndex agents

Application Development with LlamaIndex

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

Deployment and Scaling

  • Deploying LlamaIndex-based applications
  • 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 developments in LLM technology

Summary and Next Steps

Requirements

  • An understanding of Python programming and basic machine learning concepts
  • Experience with APIs and application development
  • Familiarity with natural language processing is beneficial but not required

Audience

  • Developers
  • Data scientists
 42 Hours

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  • Format: Online (live), In-company (at your offices), or Hybrid.
Investment

Price per private group, online live training, starting from 8000 € + VAT*

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