- 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
DeepSeek LLM offers powerful language generation capabilities, and advanced prompt engineering techniques allow developers to fine-tune responses, control outputs, and optimize model behavior for specific tasks.
This instructor-led, live training (online or onsite) is aimed at advanced-level AI engineers, developers, and data analysts who wish to master prompt engineering strategies to maximize the effectiveness of DeepSeek LLM in real-world applications.
By the end of this training, participants will be able to:
- Craft advanced prompts to optimize AI responses.
- Control and refine AI-generated text for accuracy and consistency.
- Leverage prompt chaining and context management techniques.
- Mitigate biases and enhance ethical AI usage in prompt engineering.
Format of the Course
- Interactive lecture and discussion.
- Lots of exercises and practice.
- Hands-on implementation in a live-lab environment.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
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