Certificate in Large Language Models and Agentic AI
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Course Overview
The Certificate in Large Language Models and Agentic AI is a 20-credit, level 9 conversion programme devised to provide up-skilling/re-skilling opportunities for numerate graduates. The programme is aimed at IT professionals with competent programming skills wishing to develop their skills in Large Language model and Agentic AI.
What makes this course different
Skills shortages in the area of data analytics has been highlighted by the Department of Enterprise, Trade and Employment, with "statisticians specialising in big data analytics with skills in IT, data mining, modelling, and advanced maths or related and relevant specialist skills" listed as a critical skill.
This course is ideal for numerate, technically capable graduates looking to enhance their skills and improve their employability in the field of Data Analytics.
Understanding the Industry
Large Language Models (LLMs) are truly disruptive technologies, poised to transform virtually every sector. For IT professionals, understanding these advancements is essential to staying relevant and competitive in a rapidly evolving landscape.
Agentic AI represents a new paradigm in application development—empowering LLMs with tools to create intelligent agents. These agents can be orchestrated to collaborate autonomously, tackling complex tasks with minimal human intervention.
In their 2025 Top Tech Trend report, Gartner predict:”By 2028, at least 15% of day-to-day work decisions will be made autonomously through agentic AI, up from 0% in 2024”.
Career Opportunities
The demand for IT professionals with AI expertise is growing rapidly, as these technologies are set to reshape many existing roles. DKIT is committed to equipping participants with an accredited qualification that employers can trust as a credible and objective measure of knowledge in this fast-evolving field.
In PluralSite’s 2025 Skills Report, they find: “50% of companies give strong preference to job candidates with AI skills”.
Course Delivery and Modules
The course will be delivered online over the course of two semesters, from September to May.
Two evening lectures (on Mondays and Wednesday evenings - although note this is subject to change) will be delivered each week during each semester.
Modules will be delivered in Python. Good Python programming skills will be required to access material and achieve the Learning Outcomes.
- Foundations of Agentic AI Systems. This module will introduce the concept of Agents and Autonomous Agentic Systems. Students will learn how to develop solutions on current Agentic frameworks, including how to develop prompts and use tools to interact with various information systems and the internet. Students will learn concepts of single agent and simple multi agent configurations. Observation tools and evaluation techniques will also be explored.
- Advanced Architectures and Applications in Agentic AI. This module will build on the concepts learned in Foundations of Agentic AI systems, exploring more complex multi agent configurations and techniques to achieve more autonomous and goal driven solutions. Students will also learn how to interact with retrieval systems and incorporate human in the loop to provide interactive solutions. A number of frameworks will be analysed and compared. The module will culminate in a project undertaken by students to demonstrate and apply their knowledge in a domain of their choosing.
- Foundations of Generative AI and LLMs. This module focuses on the foundations of generative AI and large language models (LLMs), as well as natural language processing (NLP). This module highlights the theoretical concepts, development processes, evaluation, and ethical mechanisms of generative AI. Students will engage with the latest research and tools to design and assess generative AI applications with LLMs.
- Retrieval-Augmented Generation (RAG) & Information Retrieval (IR). This module will introduce the intersection of generative AI and information retrieval. In this module, students will learn about enhancing retrieval systems to produce more accurate and context-aware output. This course will cover foundational concepts of information retrieval and retrieval-augmented generation, enabling learners to design, implement, and evaluate retrieval-augmented generative systems.
Fees and Funding
COURSE IS FREE IF YOU QUALIFY VIA ONE OF THE SPRINGBOARD CATEGORIES ELSE €400 IF EMPLOYED.
Fees
Entry requirements
This standard minimum entry requirements is a 2.2 Honours Degree (NFQ Level 8) or equivalent in STEM (Science, Technology, Engineering, and Mathematics), who have substantial technical competencies and strong programming skills. A good grounding in Maths will be required to access material related to Machine Learning
How to apply
Apply on Springboard Portal
Applications for this course are now being accepted through the Springboard portal.
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