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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 modelling and forecasting.

Deep-Think for High-Stakes Domains

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

Prompt Engineering for Deep-Think Optimisation

  • 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

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

Target Audience

  • Researchers.
  • Data scientists.
  • AI strategists.
 14 Hours

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