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Course Outline
Introduction to Generative Artificial Intelligence and Large Language Models
- Survey of generative AI technologies and their historical development
- Foundational overview of Large Language Models (LLMs), including architectures such as GPT and BERT, and their functional capabilities
- Analysis of generative models in comparison to conventional Natural Language Processing (NLP) methodologies
Transformer Architecture and Model Training Protocols
- Examination of the transformer architecture underlying LLMs
- Explanation of self-attention mechanisms and their role in language modeling
- Procedures for training large-scale language models and executing fine-tuning processes
Prompt Engineering for Optimized Interaction
- Techniques for designing prompts to ensure accuracy and utility in outputs
- Adaptation of prompt strategies to suit diverse operational applications
- Iterative testing of prompt variations to enhance response quality
Operational Applications of LLMs in the Public and Private Sectors
- Deployment of conversational AI for automated customer service
- Utilization of content generation tools for communications and media
- Application of LLMs in data analytics and automated reporting
Ethical Standards and Bias Mitigation
- Identification of potential biases within LLM-generated content
- Addressing ethical implications in the use of generative AI
- Implementation of strategies for the responsible deployment of LLMs for government and enterprise use
Advanced Methodologies in LLM Deployment
- Fine-tuning models for domain-specific requirements
- Integration of LLMs with complementary AI systems to expand functionality
- Assessment of multilingual and cross-lingual processing capabilities
Future Outlook for Generative AI in Organizational Operations
- Emerging trends in generative AI and LLM research
- Evaluation of opportunities and challenges associated with scaling LLM solutions
- Preparation for organizational transformation driven by AI technologies
Summary and Strategic Next Steps
Requirements
- Foundational knowledge of machine learning and natural language processing principles.
- Proficiency in Python programming.
Target Participants
- Data scientists and artificial intelligence professionals seeking to leverage generative AI capabilities.
- Business stakeholders evaluating automation strategies and content generation tools.
- Technical leadership and decision-makers focused on integrating large language models into operational systems for government agencies and public sector entities.
14 Hours
Testimonials (2)
The interactive style, the exercises
Tamas Tutuntzisz
Course - Introduction to Prompt Engineering
A great repository of resources for future use, instructor's style (full of good sense of humor, great level of detail)
