Prompt Engineering and Generative AI Applications

Course Category : Digital Transformation

Develop professional expertise in prompt engineering and generative AI to improve output quality, enhance productivity, support innovation, and strengthen organisational decision-making.
Duration: 5 Training Days (25 Hours)
Level: Intermediate to Advanced

Introduction

Generative artificial intelligence has transformed knowledge production, information management, and organisational workflows. Prompt engineering has consequently emerged as a critical competency for obtaining accurate, relevant, and structured outputs from advanced language models.
This course examines the foundations and advanced techniques of prompt engineering, generative AI architectures, output optimisation, and enterprise applications. It addresses content generation, information analysis, decision support, and knowledge management while emphasising reliability, information protection, and responsible AI adoption within professional environments..

Targeted Audience

IT and Digital Transformation Managers.
AI and Data Analytics Professionals.
Project and Business Development Managers.
Innovation and Organisational Improvement Specialists.
Marketing and Corporate Communication Professionals.
Human Resources and Operations Professionals.
Consultants and Business Analysts.
Professionals seeking advanced competencies in generative AI.

Targeted Skills

Understanding large language models and generative AI foundations.
Designing professional prompts aligned with organisational requirements.
Applying advanced prompt engineering techniques.
Evaluating AI outputs and identifying potential inaccuracies.
Structuring context, instructions, constraints, and response criteria.
Identifying generative AI applications across business functions.
Assessing privacy, bias, and misinformation risks.
Establishing effective and responsible enterprise AI practices.

Expected Outcomes

Explain the fundamental principles of generative AI and large language models.
Design structured prompts that produce accurate and contextually relevant responses.
Differentiate between prompt engineering techniques and select appropriate approaches.
Evaluate AI-generated content against accuracy, consistency, and reliability criteria.
Identify generative AI opportunities in reporting, analysis, and knowledge management.
Analyse risks associated with sensitive information, bias, and AI hallucinations.
Establish professional standards for prompt optimisation and output consistency.
Recommend strategic approaches for integrating generative AI into business environments.

Training Topics Index

Generative AI concepts, evolution, and enterprise applications.
Fundamental architecture of large language models and response generation.
Differences between traditional AI, generative AI, and multimodal models.
Overview of ChatGPT, Claude, Gemini, and Microsoft Copilot platforms.
Generative model capabilities, limitations, and the influence of context on output quality.

Core prompt engineering principles and components of effective prompts.
Structuring prompts through roles, context, objectives, and constraints.
Zero-shot and few-shot prompting techniques and example-based guidance.
Instruction organisation, response formatting, and quality criteria.
Diagnosing weak responses and applying iterative prompt refinement.

Multi-stage prompting and complex task decomposition.
Reasoning-oriented prompting, structured solution steps, and verification criteria.
Prompt chaining for sequential task organisation and output management.
Retrieval-Augmented Generation (RAG) and reference-grounded responses.
Prompt evaluation through accuracy, relevance, consistency, and repeatability.

Using ChatGPT and Claude for professional reporting, communication, and content development.
Microsoft Copilot applications for productivity and organisational knowledge management.
Gemini applications for information analysis, document summarisation, and idea structuring.
Generative AI use cases in marketing, human resources, and customer service.
Structured prompting for analytical support, decision-making, and multimodal outputs.

AI hallucinations, algorithmic bias, and misinformation risks.
Data protection and organisational confidentiality in generative AI environments.
Prompt injection risks and mitigation of untrusted instructions.
Enterprise standards for prompt evaluation, output review, and quality assurance.
Emerging trends in prompt engineering, multimodal AI, and agentic systems.

Course Features

Updated and Interactive Content
Hypothetical Examples and Case Studies
Pre- and Post-assessments to Measure Impact
Verified Certificate with a QR Verification Code

Prompt Engineering and Generative AI Applications