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コース概要
Introduction to Advanced Prompt Engineering
- Understanding the role of prompts in DeepSeek LLM
- How prompt structure affects AI-generated responses
- Comparing DeepSeek-R1, DeepSeek-V3, and other LLMs in prompt behavior
Designing Effective Prompts
- Crafting precise and structured prompts
- Techniques for controlling tone, length, and format
- Handling ambiguous and open-ended questions
Optimizing AI Responses
- Fine-tuning prompts for specific tasks
- Adjusting temperature and max tokens for response control
- Using system messages and role-based prompting
Context Management and Prompt Chaining
- Maintaining context across multiple AI interactions
- Chaining prompts to guide complex tasks
- Using memory and reference techniques in long conversations
Reducing Bias and Improving AI Reliability
- Detecting and mitigating biases in AI-generated outputs
- Ensuring factual accuracy in AI responses
- Ethical considerations in prompt engineering
Testing and Evaluating Prompt Performance
- Measuring AI response quality and consistency
- Automating prompt testing and evaluation
- Case studies of effective prompt engineering strategies
Deploying AI-Powered Applications with Optimized Prompts
- Integrating refined prompts into enterprise workflows
- Optimizing AI-driven chatbots and automation tools
- Scaling prompt strategies for different use cases
Emerging Trends in Prompt Engineering
- Advancements in LLMs and prompt optimization techniques
- Hybrid AI-human collaboration through prompt engineering
- Future innovations in AI-generated content control
Summary and Next Steps
要求
- Experience with large language models (LLMs) and AI APIs
- Proficiency in a programming language (eg, Python, JavaScript)
- Basic understanding of NLP and text generation techniques
Audience
- AI engineers working with LLM-based applications
- Developers optimizing AI-powered workflows
- Data analysts refining AI-generated outputs
14 時間