This course teaches you how to collaborate with agentic AI tools, not just chatbots or autocomplete, but AI that can understand your entire project, execute commands, run tests, format code, and build complete features autonomously. You'll learn to guide these tools like you would a talented junior developer on your team, setting up the right guardrails and roadmaps so they consistently deliver well-structured, maintainable code that matches your standards. Think of it as pair programming with an AI partner who learns your preferences, follows your conventions, and gets more effective the better you communicate.
By the end of this course, you'll be able to:
- Distinguish between agentic AI, chat-based AI, and autocomplete tools and choose the right approach for each situation
- Set up Cursor with Claude models (or alternatives like Claude Code, Cline, GitHub Copilot) for maximum effectiveness
- Configure cursor rules at both machine-wide and project-specific levels to teach AI your coding standards and preferences
- Create detailed implementation plans that your AI partner can follow step-by-step to build complex features
- Use source control strategically with frequent commits, staging, and rollbacks to work fearlessly with AI
- Build complete Python CLI applications from scratch with proper package structure, dependencies, and distribution setup
- Enhance existing production web applications with new features while maintaining consistency with legacy code
- Integrate custom documentation for lesser-known Python packages so AI can use them effectively
- Create reusable slash commands for repetitive review and quality assurance tasks
- Define custom agent personas (like "Brand Guardian" or "Test Reviewer") to get specialized perspectives
- Use screenshots and visual examples to communicate design intent and iterate on web interfaces
- Manage AI context windows effectively to keep conversations focused and costs under control
- Choose the right AI model for planning versus implementation based on complexity and budget
- Monitor and predict your AI usage to avoid running out of credits mid-project
- Apply "product manager" thinking to get detailed specifications and architecture plans from AI
- Structure multi-phase projects with clear milestones and deliverables
- Guide AI to self-correct when it encounters errors, missing dependencies, or type checking issues
- Ensure AI-generated code includes proper error handling, logging, and deployment-ready features
- Use parallel processing, async/await, and modern Python patterns in AI-generated code
- Create comprehensive test suites with proper coverage and configuration
- Handle both new projects and legacy codebases effectively with AI assistance
This course is perfect for:
- Python developers who have tried AI coding assistants but found them frustrating, inconsistent, or producing low-quality code
- Professional software engineers who want to dramatically increase productivity without sacrificing code quality or maintainability
- Technical leads and architects who need to evaluate and adopt AI coding tools for their teams with confidence
- Solo developers and indie hackers who want to build features and utilities that were previously too time-consuming to justify
- Anyone maintaining legacy codebases who needs to add features to older projects without creating more technical debt
- Developers skeptical of AI coding tools who want to see what's actually possible with proper setup and clear communication
Take the course over at Talk Python: training.talkpython.fm/courses/agentic-ai-programming-for-python