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Duration 42 hours
Course Outline
Foundations of LlamaIndex
- Examining LlamaIndex architecture and its function within Large Language Model (LLM) ecosystems
- Configuring the development environment and establishing necessary prerequisites
- Foundational methods for indexing proprietary data structures
Practical Implementation of LlamaIndex
- Executing queries with LlamaIndex: methodological approaches and operational standards
- Constructing query and chat engine components using LlamaIndex
- Engineering Streamlit interfaces to facilitate user interaction with LLM applications
Advanced LlamaIndex Capabilities
- Integrating Retrieval-Augmented Generation (RAG) to optimize data retrieval accuracy
- Utilizing vector stores for robust and efficient data management
- Architecting and deploying autonomous LlamaIndex agents
Building LLM Applications with LlamaIndex
- Advanced prompt engineering: chain-of-thought, ReAct, and few-shot prompting strategies
- Developing a documentation assistance tool: a case study in practical LLM application
- Implementing diagnostic and testing protocols for LLM applications
Deployment and Scalability
- Deploying LlamaIndex-based solutions into production environments
- Optimizing LLM application scalability for high-throughput performance
- Establishing monitoring frameworks and performance optimization techniques
Ethical and Operational Frameworks
- Addressing ethical implications in the deployment of LLM-driven systems
- Safeguarding privacy and data security within LlamaIndex implementations
- Anticipating future advancements in LLM technology and standards
Conclusion and Strategic Next Steps
Requirements
- Proficiency in Python programming and foundational machine learning concepts
- Experience with API integration and application development lifecycles
- Knowledge of natural language processing is advantageous but not a mandatory requirement
Intended Audience
- Software Developers
- Data Scientists
