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 Duration 14 hours

Course Outline

Introduction to Advanced Prompt Engineering

  • Comprehending the function of prompts within DeepSeek LLM
  • Analyzing how prompt architecture influences AI-generated outputs
  • Benchmarking DeepSeek-R1, DeepSeek-V3, and other LLMs regarding prompt behavior

Designing Effective Prompts

  • Constructing precise and well-structured prompts
  • Methods for directing tone, length, and output format
  • Managing ambiguous and open-ended inquiries

Optimizing AI Responses

  • Calibrating prompts for task-specific goals
  • Tuning temperature and maximum tokens for response regulation
  • Employing system messages and role-based prompting strategies

Context Management and Prompt Chaining

  • Sustaining context across multiple AI interactions
  • Linking prompts to navigate complex tasks
  • Applying memory and reference techniques in extended conversations

Reducing Bias and Improving AI Reliability

  • Identifying and mitigating biases in AI-generated content
  • Safeguarding factual precision in AI responses
  • Integrating ethical considerations into prompt engineering

Testing and Evaluating Prompt Performance

  • Assessing the quality and consistency of AI responses
  • Automating the testing and evaluation of prompts
  • Reviewing case studies on effective prompt engineering strategies

Deploying AI-Powered Applications with Optimized Prompts

  • Integrating refined prompts into enterprise-level workflows
  • Enhancing AI-driven chatbots and automation tools
  • Scaling prompt strategies across diverse use cases

Emerging Trends in Prompt Engineering

  • Recent advancements in LLMs and prompt optimization techniques
  • Fostering hybrid AI-human collaboration through prompt engineering
  • Future innovations in controlling AI-generated content

Summary and Next Steps

Requirements

  • Hands-on experience with large language models (LLMs) and AI APIs
  • Proficiency in a programming language, such as Python or JavaScript
  • Fundamental knowledge of NLP and text generation methods

Target Audience

  • AI engineers developing LLM-based solutions
  • Developers optimizing AI-driven workflows
  • Data analysts refining AI-generated data outputs

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