Advanced Prompt Engineering for DeepSeek LLM

• 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