Unit
AI Foundations for Software Delivery Teams
Amin KarajiEngineer, Founder, AuthorAI Foundations for Software Delivery Teams gives product, engineering, and delivery teams a practical understanding of how LLMs, copilots, AI assistants, and coding agents fit into modern software work. The course builds a shared baseline around AI tool behaviour, realistic use cases, output limitations, context quality, probabilistic responses, and the impact of AI on technical decision-making and team collaboration.
What you'll learn
Practical AI Understanding
- Explain fundamental AI concepts relevant to software delivery
- Distinguish between traditional software behaviour and AI-assisted outputs
- Describe the role and limitations of large language models in software delivery
- Identify limitations of large language models in software delivery
- Explain the concept of context windows and their impact on AI tool outputs
AI Tools in Software Delivery Workflows
- Explain how AI tools can support software delivery workflows
- Identify scenarios where AI tools add value in software delivery
- Identify practical limitations of AI tools in software delivery
- Outline working assumptions for using AI tools in delivery workflows
AI Limitations and Risks
- Identify typical AI failure modes in AI-generated outputs
- Recognise uncertainty in AI-generated outputs
- Explain the need for human review of AI-generated content
- Explain the need for validation of AI-generated content
- Distinguish realistic AI tool capabilities from common AI misconceptions
Communication and Collaboration
- Use appropriate terminology to describe AI concepts within software delivery teams
- Communicate AI capabilities clearly across product and engineering roles
- Communicate AI limitations clearly across product and engineering roles
- Explain AI uncertainty in a way that supports product and engineering decisions
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