Course Dates:
August 10th to 12th, from 6 PM to 9 PM Saudi time
Course Description:
Harnessing Open-Weight AI Models is a practical three-session course for professionals who want to use a purposeful mix of open-weight and frontier large language models to improve education, research, operations and business practice. The course introduces the differences between cloud-based frontier systems and locally or privately deployed open-weight models, explaining how tokens, context windows, costs, privacy and model capability shape effective use. Participants will explore tools and workflows for selecting, running and combining models, with attention to data protection, evaluation, governance and responsible implementation. Through applied examples, the course shows how open-weight models can support teaching materials, knowledge management, analysis, automation, customer insight, staff development and project delivery, while frontier models can provide complementary strengths in reasoning, multimodal work and rapid prototyping. The emphasis is on building flexible, transparent and secure AI practices that enhance professional judgement rather than replace it.
Course Objectives:
To help participants combine frontier and open-weight LLMs effectively, choosing the right model for the right task while improving productivity, privacy, cost control, reliability and professional decision-making in education, research and business settings.
Target Audience:
Business leaders, managers, analysts, consultants, academics, researchers, postgraduate students and professional staff who want to enhance their research capability through responsible and effective use of AI.
Course outline:
Frontier and Open-Weight Models
Understanding the differences between hosted frontier LLMs and open-weight models; exploring model capabilities, licensing, context windows, tokens, pricing, privacy implications and the practical trade-offs involved in choosing one model over another.
Tools for Using Open-Weight LLMs
Introducing tools and platforms for accessing, running and managing open-weight models; considering local deployment, cloud options, retrieval-augmented generation, model evaluation, data protection and integration with existing education or business workflows.
Using an Effective Harness for Your Project
Designing a practical AI harness that combines models, prompts, data, documents, evaluation checks and human oversight; applying the approach to participants’ own projects and building a repeatable workflow for responsible implementation.
About the Trainer:
Prof Jason Whittaker
Prof Jason Whittaker is a Professor of Communications at the University of Lincoln with extensive experience in communications, digital media, journalism, publishing and higher education leadership. His work includes major publications on the internet, web production, digital humanities, online journalism and artificial intelligence, including Tech Giants, Artificial Intelligence, and the Future of Journalism. A former technology journalist, he brings strong digital skills, practical media expertise and a research-led approach to professional learning.
