ICTQual AB Level 5 Diploma in AI and Prompt Engineering
The ICTQual AB Level 5 Diploma in AI and Prompt Engineering is designed to develop an advanced understanding of artificial intelligence, generative AI systems, and the strategic use of prompt engineering. This qualification explores how intelligent technologies process complex information, generate context-sensitive outputs, and support organisational objectives. It introduces learners to the evolving role of AI in business innovation, digital transformation, information management, and technology-enabled decision-making across international industries.
The qualification focuses on sophisticated prompting methodologies, AI model capabilities, contextual information management, and the design of structured AI workflows. Learners develop knowledge of how prompt architecture, task sequencing, and instruction refinement influence generated outputs. The course also examines AI performance evaluation, output quality assurance, and methods for identifying limitations in AI-generated information. These areas support a deeper understanding of how artificial intelligence can be applied to complex tasks while maintaining relevance, consistency, and reliability.
The ICTQual AB Level 5 Diploma in AI and Prompt Engineering is suitable for individuals seeking to strengthen their understanding of advanced AI applications and emerging digital technologies. It explores the relationship between prompt engineering, organisational efficiency, and responsible technology use. The qualification also emphasises data confidentiality, ethical considerations, information accuracy, and appropriate human oversight. By studying these concepts, learners can develop a broader understanding of AI-enabled processes and their significance in modern professional environments.
Level 5 Diploma in AI and Prompt Engineering
To enrol in the ICTQual AB Level 5 Diploma in AI and Prompt Engineering, learners should meet the following requirements:
The ICTQual AB Level 5 Diploma in AI and Prompt Engineering, qualification consisting of 6 mandatory study units.
- Advanced AI Systems and Foundation Models
- Prompt Architecture and Multi-Step AI Workflows
- Knowledge-Grounded AI and Retrieval Techniques
- AI Automation and Intelligent Workflow Integration
- AI Evaluation, Security and Adversarial Prompting
- Professional AI Solution Development Project
Here are the learning outcomes for each study unit:
Advanced AI Systems and Foundation Models
- Explain the architecture, capabilities, and limitations of advanced AI systems and foundation models.
- Analyse how foundation models process information and generate responses across different applications.
- Compare AI models according to their suitability for specific organisational and information-processing requirements.
- Evaluate factors affecting model performance, reliability, scalability, and output quality.
Prompt Architecture and Multi-Step AI Workflows
- Design structured prompt architectures for complex tasks involving multiple instructions and objectives.
- Develop multi-step AI workflows that organise tasks into logical and manageable stages.
- Apply sequencing, conditional instructions, and contextual information to improve workflow consistency.
- Evaluate prompt structures and workflow designs against defined performance requirements.
Knowledge-Grounded AI and Retrieval Techniques
- Explain the principles of knowledge-grounded AI and retrieval-based information processing.
- Apply retrieval techniques to incorporate relevant reference materials into AI-generated responses.
- Structure supporting information to improve contextual relevance and reduce unsupported outputs.
- Evaluate the quality, relevance, and reliability of retrieved information used by AI systems.
AI Automation and Intelligent Workflow Integration
- Identify opportunities to integrate AI technologies into organisational processes and digital workflows.
- Design AI-assisted workflows that coordinate information inputs, processing stages, and expected outputs.
- Evaluate automation approaches according to efficiency, reliability, and operational requirements.
- Recognise the importance of human oversight, process monitoring, and appropriate control measures in AI-enabled workflows.
AI Evaluation, Security and Adversarial Prompting
- Establish evaluation criteria for assessing AI accuracy, consistency, security, and response quality.
- Identify risks associated with adversarial prompts, prompt injection, and manipulated AI instructions.
- Analyse potential vulnerabilities involving confidential information, unauthorised outputs, and unreliable responses.
- Recommend suitable safeguards, testing methods, and monitoring practices to support secure and responsible AI use.
Professional AI Solution Development Project
- Define an AI-related problem, establish project objectives, and determine appropriate technical requirements.
- Develop a structured AI solution using suitable models, prompt architectures, and workflow methods.
- Evaluate the proposed solution against performance, reliability, security, and usability criteria.
- Present project findings and recommendations while addressing limitations, ethical considerations, and responsible AI implementation.
The ICTQual AB Level 5 Diploma in AI and Prompt Engineering develops knowledge of advanced AI systems, intelligent workflows, and secure AI solution design. These competencies can support professional development across AI-driven industries and technology-enabled organisations.
- Develop expertise in foundation models and advanced generative AI applications across different business environments.
- Explore AI workflow architecture to improve process coordination and information handling.
- Apply knowledge-grounded AI techniques to support reliable information retrieval and response generation.
- Strengthen understanding of intelligent automation and AI integration within organisational processes.
- Develop skills in AI evaluation, performance monitoring, and output quality assurance.
- Understand AI security principles, prompt injection risks, and methods for protecting AI-enabled systems.
- Contribute to AI solution development by assessing requirements, evaluating outcomes, and identifying improvements.
- Explore opportunities in AI-assisted operations, workflow automation, prompt engineering, digital transformation, and AI solution support, depending on individual competencies and employer requirements.
