AI curriculum
Course outline for Building Applications with LLMs
A model demo does not provide application reliability. Build explicit interfaces, state handling, and review paths for a small LLM-powered application.
About Building Applications with LLMs
A model demo does not provide application reliability. Build explicit interfaces, state handling, and review paths for a small LLM-powered application.
Building Applications with LLMs Course Objectives
- Design a bounded LLM application architecture.
- Validate structured output and tool arguments.
- Implement failure handling and a human review step.
Pre-requisites
- Ability to build a small application in one language.
- Familiarity with APIs and JSON.
Lab Setup
- Computer with a code editor, language runtime, and test runner.
- Use a local model or mocked responses and synthetic data; remote model access is optional.
Detailed Course Outline
Proposed modulesModule 1: Application interfaces
- LLM application architecture
- Prompt and context design
- Structured generation
- Function calling
Practical outcome: Specify model inputs and validated output contracts.
Module 2: Controlled execution
- Tool integration
- State management
- Error handling
- Human-in-the-loop interaction
Practical outcome: Implement a recoverable interaction with an approval boundary.
Practical exercise
- Build a small assistant with a mock tool, output validation, and a review queue.
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.
Discuss your learning goals