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Duration 42 hours
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
