Upcoming Courses

Register and pay 30 days prior to the course commencement date to receive a 10% early bird discount. Or register a group of 3+ for a 10% group discount. Available for corporate training worldwide.

• Learn the strengths and limitations of LLMs in an SE environment
• Apply prompt engineering to diagnose defects in and rewrite system requirements
• Apply prompt engineering to architect a workshop system, defining and evaluating alternative physical architectures
• For the chosen system physical architecture, apply prompt engineering to the derivation of subsystem requirements in satisfaction of system requirements
• Reflect on data security, confidentiality, and responsibility considerations when using AI in an engineering environment
• Consider the migration path from AI supporting the individual engineer only, to AI at scale and integral to engineering organizational workflows.

The workshop is evenly split between presentation/discussion mode and handson workshop activity using input data sets integral to the courseware. The workshop event is “BYOL – Bring Your Own LLM”, for example ChatGPT, Gemini, Claude or Copilot. No learning of software is needed. Courseware is delivered by download.

Learning Outcomes

By the end of the workshop, participants will have a practical understanding of where AI fits into the systems engineering workflow, have confidence in applying prompt patterns to real SE problems, and be aware of the safeguards needed to use AI responsibly and safely in engineering practice.

Duration

The workshop, of one day total duration, is delivered in-person or online, and may be taken over a single day or over two half-days, as desired when taken “inhouse” by corporate clients. Publicity for given open-registration deliveries indicates which delivery option – one or two days – applies.

1. Workshop Orientation (0.5 hours)

• Purpose and scope
• Definitions of key terms – AI, LLM, prompt, hallucination
• Connection to existing systems engineering practice
• Path to enterprise-scalable AI4SE

2. AI and LLM Fundamentals for SE (0.5 hours)

• What LLMs do, and do not do
• LLM glossary
• How LLMs work
• Trade-offs in the use of LLMs
• LLM limitations 
• AI capability stack

3. Prompt Engineering (1.5 hours)

• A prompt engineering process model
• The anatomy of an effective prompt
• A comprehensive illustrated prompting strategy
• Creativity and reproducibility in LLMs
• Quality attributes of LLM outputs
• Observed LLM defects
• Hallucination prevention methods
• Integration of AI outputs into engineering artifacts
• Organizational management of LLMs 

4. Applied Practice – Building SE Artifacts (4.5 hours)

• Introduction to the workshop system
• Exercise 1 – System requirements quality (in depth)
• Exercise 2 – System architectural design (in depth)
• Exercise 3 – Requirements allocation; subsystem requirements derivation (in depth)

5. Enterprise Risks, Governance, and Adoption Roadmap (0.9 hours)

• Proceed with caution!
• A roadmap towards safe, enterprise-scalable AI-enabled engineering
• Example AI Use Policy
• AI governance
• Development and governance process model
• LLM security boundaries
• Retrieval Augmented Generation (RAG) implementation architectures
• Apply engineering thinking and tools in the application of AI to SE
• References and further reading

6. In Closing (0.1 hours)

Note: The timing of workshop modules is indicative and is subject to some variation between deliveries.