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Duration 42 hours
Course Outline
Introduction to LlamaIndex
- Understanding LlamaIndex and its role in the context of LLMs.
- Setting up LlamaIndex: environment configuration and prerequisites.
- Foundations 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 vector stores for efficient data management.
- Designing and implementing LlamaIndex agents.
Application Development with LlamaIndex
- Prompt engineering: chain of thought, ReAct, and few-shot prompting.
- Developing a documentation helper: a real-world LLM application case study.
- Debugging and testing LLM applications.
Deployment and Scaling
- Deploying applications based on LlamaIndex.
- 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
- Proficiency in Python programming and foundational machine learning concepts.
- Experience with APIs and application development.
- Familiarity with natural language processing is advantageous but not mandatory.
Target Audience
- Developers
- Data scientists