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Course Outline
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
Requirements
- Experience with large language models (LLMs) and AI APIs.
- Proficiency in a programming language (e.g., 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 Hours
