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Duration 7 hours
Course Outline
Foundational Overview of Artificial Intelligence
- Broad assessment of artificial intelligence and its influence on various economic sectors
- Core principles of machine learning and deep learning methodologies
- Historical progression of artificial intelligence technologies and their contemporary operational capabilities
Generative Artificial Intelligence and Large Language Models (LLM)
- Analysis of generative artificial intelligence systems and their professional applications.
- Examination of the structural design and functional attributes of ChatGPT
- Critical review of data privacy protocols and security considerations
- Best practices in prompt engineering for precise output generation
Principles of Retrieval Augmented Generation (RAG)
- Definition of RAG frameworks and their role in augmenting generative model performance
- Strategy for integrating retrieval mechanisms to enhance contextual accuracy in generative models
- Practical session: Deployment of a foundational RAG architecture for targeted information retrieval
Utilizing GPT and RAG for Unstructured Data Analysis
- Methodologies for deriving actionable insights from unstructured datasets using GPT and RAG
- Applied case study: Processing textual records to generate operational intelligence
Applying GPT to Structured Data Analysis
- Implementation of GPT models for the evaluation of structured datasets
- Protocols for converting structured data into formats compatible with GPT processing
Artificial Intelligence Tools and Technical Platforms
- ChatGPT
- Claud
- Copilot
- LlamaIndex
- Langchain
