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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
 7 Hours

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