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Course Outline
Introduction to Prompting and ChatGPT for Government
- Basics of Prompting - Prompt Structure & Prompt Styles for Government
- Pitfalls of Large Language Model (LLM) Use in Government
- Open AI Models – Definitions & Types Relevant to Government Operations
- Need and Working Principles of ChatGPT for Government
- Capabilities of ChatGPT in Public Sector Applications
- Key Concepts of GPT-3.5 for Government Use
- Demonstration: Account Creation (PaaS / SaaS) for Government Agencies
Applications of Effective Prompting using ChatGPT for Government
- Daily Tasks - Summary Generation, Proofreading, Language Translation, Email Writing, Blog Writing, Cold Sales Emails, etc., for Government Operations
- Technical Tasks – Excel Formula Creation, Code Writing, Code Debugging, etc., for Government IT Systems
- Creativity Tasks – Headline/Tagline Creation, Content Creation, Blog Writing, etc., for Government Communications
Different OpenAI Applications for Government
- GPT-3.5 Playground – Fine-Tuning of ChatGPT for Government Needs
- DALL.E 2 – Image Creation and Editing for Government Publications
- Codex – Natural Language to Animation Creation for Government Training Materials
Techniques of Image Prompting for Government
- Need for Image Prompting in Government Communications and Documentation
- Advanced Techniques - Style Modifiers, Quality Boosters, Repetition, Weighted Terms, Fix Deformed Generations, etc.
- Use Cases - DALL.E 2, Stable Diffusion & Midjourney in Government Applications
Enhancing Prompt Reliability for Government
- Promote Debiasing and Ensemble Learning in Government Models
- Address Transparency and Privacy Concerns in Government Use of AI
- Use Cases of Prompt Reliability in Government Operations
Requirements
1. Python Programming: Proficiency in Python is essential, as it is the primary language used by deep learning libraries such as PyTorch and TensorFlow, which are critical for government applications.
2. Machine Learning: A solid understanding of basic machine learning concepts, techniques, and algorithms is necessary to grasp the foundational elements that underpin advanced models like ChatGPT.
3. Deep Learning: Knowledge of neural networks, particularly recurrent neural networks (RNNs) and transformers, is crucial for effectively fine-tuning and managing complex models such as GPT in a government context.
4. Natural Language Processing (NLP): Familiarity with common NLP techniques and libraries like NLTK and Spacy, along with an understanding of context generation and text generation algorithms, is vital for enhancing the capabilities of language models for government use.
5. Reinforcement Learning: Given that GPT-3 incorporates reinforcement learning from human feedback, a basic understanding of reinforcement learning principles and reward systems can be beneficial in optimizing these models for government applications.
Additionally, experience with cloud platforms such as Google Cloud, Microsoft Azure, or AWS, along with foundational skills in data storage, manipulation, and processing (using tools like SQL, pandas, and NumPy), is valuable when working with large datasets and deploying models in a public sector environment.
7 Hours
