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Course Outline

Foundations of Advanced Prompt Engineering

  • The role of prompts in shaping DeepSeek LLM performance
  • The impact of prompt structure on AI-generated results
  • Comparative analysis of prompt behavior in DeepSeek-R1, DeepSeek-V3, and other LLMs

Creating Effective Prompts

  • Constructing precise, well-structured prompts
  • Making techniques for regulating tone, length, and output format
  • Approaching ambiguous or open-ended inquiries

Enhancing AI Response Quality

  • Refining prompts for specific use cases
  • Managing response control via temperature and max token settings
  • Applying system messages and role-based prompting strategies

Managing Context and Implementing Prompt Chaining

  • Sustaining context across successive AI interactions
  • Using prompt chains to direct complex tasks
  • Incorporating memory and reference methods in extended conversations

Mitigating Bias and Bolstering AI Reliability

  • Identifying and reducing biases in AI-generated content
  • Verifying factual accuracy in model responses
  • Addressing ethical dimensions of prompt engineering

Assessing and Validating Prompt Performance

  • Evaluating the quality and consistency of AI outputs
  • Automating prompt testing and validation workflows
  • Reviewing case studies of successful prompt engineering

Implementing AI Applications with Refined Prompts

  • Embedding optimized prompts into enterprise processes
  • Enhancing AI-powered chatbots and automated tools
  • Adapting prompt strategies for diverse use cases

Current Trends in Prompt Engineering

  • Recent developments in LLMs and prompt optimization methods
  • Facilitating hybrid AI-human collaboration via prompts
  • Future directions in controlling AI-generated content

Wrap-Up and Future Directions

Requirements

  • Working experience with Large Language Models (LLMs) and AI APIs
  • Competence in a programming language (e.g., Python, JavaScript)
  • Foundational knowledge of NLP and text generation processes

Intended Audience

  • AI engineers developing LLM-based solutions
  • Developers enhancing AI-driven workflows
  • Data analysts improving the quality of AI outputs
 14 Hours

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