Tabnine for Advanced Developers Training in Astana, Kazakhstan

This course is designed for experienced developers who want to master Tabnine as an AI code assistant. The training covers advanced usage, customization, integration into professional workflows, and techniques to boost productivity, maintain security, and ensure code quality.

Fundamentals and Advanced Usage

Introduction to Tabnine

• What is Tabnine and how it works
• Key differences from Copilot, Cody, and other AI tools
• Local vs cloud vs enterprise models
• Supported languages and IDEs

Installation and Configuration

• Installing Tabnine in VSCode, JetBrains, Vim
• Accessing and using Tabnine Hub
• Initial setup and personal preferences
• Configuring global and per-project settings

Advanced Code Completion Techniques

• Understanding how Tabnine predicts code
• Manual triggering vs inline suggestions
• Completing function bodies, conditionals, and test cases
• Using Tabnine for repeated coding patterns and boilerplate

Team Integration and Workflow Optimization

Tabnine for Teams and Enterprises

• Using Tabnine Team Hub
• Managing shared models and team learning
• Enabling/disabling completions per file or user
• Privacy controls and project isolation

IDE Workflow Optimization

• Using Tabnine efficiently in large projects
• Custom shortcuts and completion triggers
• Tabnine with Git, linters, and formatters
• Reducing noise and irrelevant suggestions

DevOps and CI/CD Integration

• Using Tabnine suggestions in pull requests
• Test generation and increasing code coverage
• Incorporating Tabnine into CI/CD pipelines
• Validating suggestions with static analysis tools

Customization, Security, and Evaluation

Customizing Tabnine for Your Stack

• Training Tabnine on internal/private code
• Prioritizing internal libraries and frameworks
• Project-specific behavior tuning
• Adjusting suggestion style to match team conventions

Security and Compliance

• Managing code privacy and local-only setups
• Avoiding generation of insecure or unlicensed code
• Setting blocklists and pattern filters
• Using air-gapped deployment options

Evaluation and Monitoring

• Using Tabnine analytics and feedback tools
• Measuring developer productivity and time savings
• Collecting user feedback and refining usage
• Recognizing limitations and model hallucinations

Final Assessment and Use Case Presentation

• Complete a sprint-sized task using Tabnine
• Compare performance with and without AI
• Present findings on quality, efficiency, and collaboration
• Discuss trends and future roadmap of AI in development