Course Outline

Foundations of Deep-Think Mode

  • Understanding Deep-Think architecture
  • Depth vs breadth reasoning patterns
  • Evaluating when Deep-Think is appropriate

Long-Context Reasoning

  • Handling extended input sequences
  • Maintaining coherence across long outputs
  • Tracking dependencies and constraints

Iterative and Multi-Step Problem Solving

  • Designing stepwise reasoning prompts
  • Validating intermediate conclusions
  • Building reasoning loops and refinements

Advanced Analytical Workflows

  • Structuring complex research questions
  • Data-driven reasoning pipelines
  • Scenario modeling and forecasting

Deep-Think for High-Stakes Domains

  • Risk-sensitive problem framing
  • Evaluating critical decisions
  • Ensuring consistency and traceability

Prompt Engineering for Deep-Think Optimization

  • Constructing high-yield prompts
  • Shaping the model’s internal reasoning path
  • Managing ambiguity and uncertainty

Integrating Deep-Think into Applications

  • Combining Deep-Think with multimodal inputs
  • Embedding reasoning features into workflows
  • Automation and system-level orchestration

Evaluation and Refinement Techniques

  • Assessing reasoning quality and reliability
  • Error analysis and correction patterns
  • Continuous improvement of reasoning pipelines

Summary and Next Steps

Requirements

  • An understanding of machine learning principles
  • Experience with Python-based AI workflows
  • Familiarity with API-driven model integration

Audience

  • Researchers
  • Data scientists
  • AI strategists
 14 Hours

Delivery Options

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