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Course Outline
Overview of Advanced Prompt Engineering
- The function of prompts within DeepSeek Large Language Models (LLMs)
- The influence of prompt architecture on AI-generated outputs
- A comparison of prompting behaviors across DeepSeek-R1, DeepSeek-V3, and other LLMs
Formulating Effective Prompts
- Developing precise and structured prompts
- Strategies for controlling tone, length, and format
- Addressing ambiguous or open-ended inquiries
Optimizing AI Responses
- Tailoring prompts for specific operational tasks
- Adjusting temperature and maximum token parameters for response control
- Leveraging system messages and role-based prompting techniques
Context Management and Prompt Chaining
- Maintaining context across multiple AI interactions
- Utilizing prompt chaining to facilitate complex tasks
- Applying memory and reference techniques in extended conversations
Mitigating Bias and Enhancing AI Reliability
- Identifying and reducing biases in AI-generated outputs
- Ensuring factual accuracy in AI responses
- Ethical considerations in prompt engineering for government
Testing and Evaluating Prompt Performance
- Assessing the quality and consistency of AI responses
- Automating prompt testing and evaluation processes
- Case studies demonstrating effective prompt engineering strategies
Deploying AI-Powered Applications with Optimized Prompts
- Integrating refined prompts into enterprise workflows
- Optimizing AI-driven chatbots and automation tools for government operations
- Scaling prompt strategies to accommodate diverse use cases
Emerging Trends in Prompt Engineering
- Advancements in LLMs and prompt optimization techniques
- Hybrid AI-human collaboration through prompt engineering
- Future innovations in controlling AI-generated content for government applications
Summary and Next Steps
Requirements
- Familiarity with large language models (LLMs) and application programming interfaces (APIs) for artificial intelligence
- Competence in a primary programming language, such as Python or JavaScript
- Foundational knowledge of natural language processing (NLP) and text generation methodologies
Intended Audience
- AI engineers developing LLM-based solutions for government
- Developers enhancing efficiency within AI-driven operational workflows
- Data analysts evaluating and refining outputs generated by artificial intelligence systems
14 Hours
