Course overview
Transform how you communicate with large language models. This intensive, hands-on program teaches the principles and practice of prompt engineering for modern LLMs (ChatGPT, GPT-4 and similar models). Learners will master a proven set of techniques to reliably shape outputs, extract high-value insights, and optimize model performance for real-world tasks.

Why this course

• Practical focus: workshops, real-world case studies, and iterative feedback cycles.

• Framework-driven: learn a repeatable prompt-design method (instruction, context, examples, persona, format, tone) to improve consistency and control

• Tool-ready: apply techniques across ChatGPT/GPT-4 and complementary AI tools used in industry workflows

Course structure

Foundation & Theory

• Modern LLM architectures and capabilities (ChatGPT, GPT-4, distinctions from GPT-3.5)

• Core prompt engineering principles and behavioral mechanics

• Contextual conversation design and session-state management

• Response quality metrics and performance boundaries

Practical Applications

• Hands-on prompt-crafting labs with iterative testing and evaluation

• Industry-specific use cases (marketing, product, data, support, engineering)

• Peer review & instructor feedback sessions

• Performance tuning and evaluation exercises

Core modules (7)

Module 1 — ChatGPT & LLM Essentials

• LLM architectures, strengths, and limitations

• Model behavior, safety considerations, and hallucination mitigation

Module 2 — Engineering Fundamentals

• Core prompt-building blocks and decomposition

• Output-targeting techniques and common pitfalls

Module 3 — Context Mastery

• Structuring background info and conversational state

• Multi-turn flow control and context-window strategies

Module 4 — Instruction Design

• Precision instruction writing and constraints

• Behavioral guidance (system messages, role-playing, guardrails)

Module 5 — Question Engineering

• Strategic question framing for accuracy and depth

• Techniques for extracting structured and unstructured information

Module 6 — Format & Interaction Optimization

• Using system messages, templates, and output schemas to control format

• UX patterns for API-based and chat-based integrations

Module 7 — Advanced Optimization & Customization

• Iterative refinement and evaluation loops

• Domain-specific prompt libraries and fine-tuning strategies

• Monitoring, metrics, and maintenance of prompt-driven systems

Learning outcomes
By completing this program you will be able to:

• Design precise prompts that produce reliable, high-quality outputs

• Optimize context and session flow for multi-turn interactions

• Create instruction templates and output schemas to meet business needs

• Formulate targeted questions that maximize information extraction

• Implement monitoring and iterative refinement processes for production use