Overview
AI Integration in Software Development is a technical, project-based course that teaches developers to add reliable AI capabilities to software applications. Participants move from use-case selection and solution architecture to model and API integration, prompt design, structured outputs, retrieval, tool calling, testing and production monitoring. The course also addresses latency, cost, privacy, security, failure handling and human review. Learners complete a working AI-enabled application or software feature.
Course Highlights
AI use-case and architecture design
Model and API integration
Prompting and structured outputs
Retrieval and knowledge integration
AI testing and evaluation
Deployment, security and monitoring
Modules & Curriculum
AI Use Cases and Software Solution Architecture
Model, API and Technology Selection
Prompt Design, Context and Structured Outputs
Retrieval and Organisational Knowledge Integration
Tool Calling and Application Workflows
AI Testing, Evaluation, Safety and Security
Deployment, Latency, Cost and Monitoring
AI-Enabled Software Capstone Project
Learning Outcomes
- Identify suitable AI capabilities for software products and user workflows.
- Design an appropriate architecture for an AI-enabled application.
- Select and integrate suitable models, APIs and supporting services.
- Develop prompts, context strategies and structured application outputs.
- Connect AI applications to organisational knowledge through retrieval.
- Implement tool calling and multi-step application workflows.
- Test AI features for quality, reliability, safety and failure conditions.
- Manage latency, cost, privacy, security and human-review requirements.
- Deploy and monitor a functional AI-enabled application or feature.
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