AI Builder Launchpad
Build practical AI capability from fundamentals to enterprise-ready RAG, automation, evaluation and real-world AI projects.
Learn AI by building
Move from understanding AI concepts to building practical AI applications. The Launchpad is structured around hands-on learning, reusable patterns and real-world implementation.
- 10 AI foundation lessons
- Advanced RAG and production practices
- Practical AI projects
- Reusable code and implementation patterns
Build your AI foundation
Ten practical lessons covering the essential concepts and development practices needed to start building AI applications.
Continue building your AI foundation one lesson at a time.
AI Stack
Continue
Understand models, applications, data, infrastructure and the components behind modern AI systems.AI Use Cases
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Identify practical AI opportunities and connect them to measurable business outcomes.Baseline Prompts
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Learn structured prompting techniques for more consistent and useful AI responses.Evaluate Responses
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Learn practical approaches for evaluating and improving AI output quality.Python Setup
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Set up a practical development environment for building AI applications.Structured Outputs
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Learn how to make AI responses predictable, structured and easier to use in applications.Add Context
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Improve AI responses by supplying relevant context and information.Control Cost
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Learn practical techniques for controlling AI usage, latency and cost.Safety Rails
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Add practical safety controls and guardrails to AI applications.Mini Assistant
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Bring the fundamentals together by building a practical AI assistant.AI Stack
Understand models, applications, data, infrastructure and the components behind modern AI systems.
AI Use Cases
Identify practical AI opportunities and connect them to measurable business outcomes.
Baseline Prompts
Learn structured prompting techniques for more consistent and useful AI responses.
Evaluate Responses
Learn practical approaches for evaluating and improving AI output quality.
Python Setup
Set up a practical development environment for building AI applications.
Structured Outputs
Learn how to make AI responses predictable, structured and easier to use in applications.
Add Context
Improve AI responses by supplying relevant context and information.
Control Cost
Learn practical techniques for controlling AI usage, latency and cost.
Safety Rails
Add practical safety controls and guardrails to AI applications.
Mini Assistant
Bring the fundamentals together by building a practical AI assistant.
Go deeper into enterprise AI
Progress into vector databases, retrieval-augmented generation and practical production AI practices.
Vector Database Foundations
Understand embeddings, vector search and the foundations of modern knowledge retrieval.
RAG Architecture
Learn how retrieval-augmented generation connects enterprise knowledge with AI models.
Production Habits
Build practical habits around observability, evaluation, reliability, security and deployment.
Turn learning into working AI
Apply the concepts through practical projects that demonstrate how AI systems are designed and built.
Personal Knowledge RAG
Build a practical retrieval-augmented generation application for personal or enterprise knowledge.
Support Triage Assistant
Build an AI assistant that can classify and route support requests.
Research Copilot
Build a practical AI copilot for research and information synthesis.
Course Mentor Bot
Build an AI mentor experience to support structured learning.
Build with reusable examples
Access the Launchpad learning material and practical project code, including foundation exercises, advanced AI patterns and complete project examples.