AI curriculum
Course outline for Applied Generative AI
Useful AI outputs need more than a single prompt. Design structured, context-aware interactions and select models for practical generation and classification tasks.
About Applied Generative AI
Useful AI outputs need more than a single prompt. Design structured, context-aware interactions and select models for practical generation and classification tasks.
Applied Generative AI Course Objectives
- Write prompts with explicit output requirements.
- Define a tool interface and validate structured results.
- Compare model choices for a task using sample outputs.
Pre-requisites
- Familiarity with common AI interfaces.
- Basic understanding of structured data such as JSON.
Lab Setup
- Computer with a browser and a JSON editor.
- Local model or supplied response fixtures; optional service access must use a nonproduction account.
- Use synthetic documents rather than confidential data.
Detailed Course Outline
Proposed modulesModule 1: Defining interactions
- Prompting
- Structured outputs
- Tool use
Practical outcome: Specify a prompt and a valid output schema.
Module 2: Selecting execution paths
- Function calling
- Model selection
- Model routing
Practical outcome: Design a routing rule and a bounded function interface.
Module 3: Supplying task context
- Context engineering
- Multimodal interaction
- Decision and classification models
Practical outcome: Compare context choices for a classification task.
Practical exercise
- Prototype a document classification interaction and review its outputs against explicit criteria.
How we train
Contact us for full course details, including duration, delivery options and lab requirements.
Ask about exercises, instructor feedback and the prior knowledge you need. Tool-specific courses marked provisional may become modules in a broader course.
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