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Duration 14 hours
Course Outline
Introduction to Advanced Prompt Engineering
- Comprehending the function of prompts within DeepSeek LLM
- Analyzing how prompt architecture influences AI-generated outputs
- Benchmarking DeepSeek-R1, DeepSeek-V3, and other LLMs regarding prompt behavior
Designing Effective Prompts
- Constructing precise and well-structured prompts
- Methods for directing tone, length, and output format
- Managing ambiguous and open-ended inquiries
Optimizing AI Responses
- Calibrating prompts for task-specific goals
- Tuning temperature and maximum tokens for response regulation
- Employing system messages and role-based prompting strategies
Context Management and Prompt Chaining
- Sustaining context across multiple AI interactions
- Linking prompts to navigate complex tasks
- Applying memory and reference techniques in extended conversations
Reducing Bias and Improving AI Reliability
- Identifying and mitigating biases in AI-generated content
- Safeguarding factual precision in AI responses
- Integrating ethical considerations into prompt engineering
Testing and Evaluating Prompt Performance
- Assessing the quality and consistency of AI responses
- Automating the testing and evaluation of prompts
- Reviewing case studies on effective prompt engineering strategies
Deploying AI-Powered Applications with Optimized Prompts
- Integrating refined prompts into enterprise-level workflows
- Enhancing AI-driven chatbots and automation tools
- Scaling prompt strategies across diverse use cases
Emerging Trends in Prompt Engineering
- Recent advancements in LLMs and prompt optimization techniques
- Fostering hybrid AI-human collaboration through prompt engineering
- Future innovations in controlling AI-generated content
Summary and Next Steps
Requirements
- Hands-on experience with large language models (LLMs) and AI APIs
- Proficiency in a programming language, such as Python or JavaScript
- Fundamental knowledge of NLP and text generation methods
Target Audience
- AI engineers developing LLM-based solutions
- Developers optimizing AI-driven workflows
- Data analysts refining AI-generated data outputs