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Duration 14 hours (2 days)
Course Outline
Introduction to Advanced Cursor Capabilities
- Comprehending Cursor’s extensibility and underlying architecture
- Examining various AI model types and their integration points
- Setting up the environment for advanced customization tasks
Principles of Effective Prompt Engineering
- Crafting prompts for precision, consistency, and adaptability
- Structuring context hierarchies and managing variable injection
- Evaluating prompt outputs and refining iterative improvements
Building and Managing Prompt Templates
- Creating reusable prompt templates for team utilization
- Implementing version control and maintenance for template repositories
- Integrating prompt templates into CI/CD pipelines
Integrating Cursor with Internal Knowledge Bases
- Connecting to documentation APIs and internal data sources
- Embedding domain-specific knowledge into AI prompts
- Automating updates and synchronization for dynamic data sets
Fine-Tuning Models for Domain-Specific Code Generation
- Identifying optimal use cases for fine-tuned models
- Collecting and curating high-quality fine-tuning datasets
- Testing, validating, and deploying custom-trained models
Developing Custom Tools and Adapters
- Extending Cursor with API-based custom tooling solutions
- Creating secure adapters tailored for enterprise workflows
- Implementing custom actions directly within the editor interface
Security, Governance, and Performance Optimization
- Ensuring secure handling of AI-generated code
- Establishing policy guards and compliance filters
- Optimizing performance and managing resources efficiently
Future-Ready AI Development Strategies
- Evaluating emerging Cursor features and new APIs
- Adopting continuous fine-tuning and prompt lifecycle management
- Building internal frameworks for sustainable AI engineering practices
Summary and Next Steps
Requirements
- A solid grasp of programming principles and software architecture
- Practical experience with AI-assisted coding tools and API integrations
- Familiarity with machine learning or prompt engineering concepts
Target Audience
- AI engineers designing custom AI workflows
- Tooling and platform engineers building internal developer tools
- Senior developers integrating domain-specific AI models