MASTERING GENERATIVE AI & PROMPT ENGINEERING

19th-20th February, 2026                                                                                                                 22nd-23rd April, 2026

23^rd-24^th July, 2026                                                                                                                              1^st-2^nd December, 2026

Introduction

Generative AI (GenAI) refers to AI models, such as LLMs, that create new content (text, images, code) using trained data. Prompt engineering is the essential skill of crafting, refining, and optimizing these inputs (prompts) to guide models toward producing accurate, relevant, and high-quality outputs. Key techniques include defining roles, providing context, and using examples to improve performance. 

Generative AI and Prompt Engineering covers designing, testing, and refining prompts to optimize AI outputs, spanning foundational LLM concepts to advanced techniques like chain-of-thought, persona adoption, and
multimodal prompting. Key modules typically include prompt structure, parameter tuning (temperature/top_p),
ethical considerations, and practical applications in text, code, and image

Key Aspects of Generative AI & Prompt Engineering:

• Definition & Importance: Prompt engineering acts as a bridge between human intent and AI capability, ensuring the generated output is precise and useful. It is considered a crucial, high-value skill for maximizing
  productivity with tools like ChatGPT, DALL-E, and GitHub Copilot.
• Components of a Good Prompt: Effective prompts often include specific instructions, context, input data, and desired output formats (e.g., tone, length, structure).

Course Contents

Module One: Foundations of Generative AI

• AI & LLM Mechanics: Understanding Large Language Models (LLMs), tokenization, and how transformers process language.
• Model Architectures: Exploring Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs),
  and Diffusion models for image generation.
• Neural Networks: Core concepts like backpropagation, deep learning architectures, and Natural
  Language Processing (NLP) implications. 

Module Two: Core Prompt Engineering Techniques

• Zero-Shot & Few-Shot Prompting: Learning to get results with no examples versus providing a few demonstrations to guide the model.
• Chain-of-Thought (CoT): Guiding the AI to show its “reasoning” steps to solve complex logic or math
  problems.
• Role/Persona Prompting: Instructing the AI to adopt a specific professional or historical persona to influence
  tone and expertise.
• Iterative Refinement: Methods for systematically tweaking prompts (Role, Task, Context, Output Format) to
  improve accuracy. 

Module Three: Advanced & Agentic AI Strategies

• Reasoning Frameworks: Complex strategies like Tree of Thoughts (ToT), Least-to-Most prompting, and Self-Consistency.
• Agentic AI & Autonomy: Designing autonomous agents using tools like LangChain, CrewAI, or AutoGen that can plan and execute multi-step tasks.
• Retrieval-Augmented Generation (RAG): Integrating external data (vector databases) to ground AI responses in factual, private, or up-to-date information.
• Meta-Prompting: Using AI to write or optimize prompts for other AI models. 

Module Four: Specialized Applications & Tools

• Multimodal Prompting: Techniques for generating and refining images (e.g., Midjourney, DALL-E) and
  audio/video.
• Development Tools: Hands-on labs with IBM watsonx, OpenAI Playground, and Python-based libraries like DSPy for prompt optimization.
• Code Generation: Leveraging AI for programming, debugging, and software development. 

Module Five:  Ethics, Security & Governance

• Security Threats: Identifying and defending against Prompt Injection attacks, jailbreaking, and data leakage.
• Bias & Fairness: Strategies for detecting and mitigating hallucinations, gender/racial bias, and ensuring responsible AI usage.
• Evaluation Metrics: Using frameworks like RAGAs or metrics like BLEU and ROUGE to measure AI
  performance.

Duration: Two (2) days                                                                 Fee: N180,000