TRAINING FOR COMPANIES
Program better and faster with AI assistants
Training for development teams in professional use of AI to code: Copilot, Cursor, effective prompting, critical review and limits.
What is AI Scheduling and Prompting?
AI assistants for programming (GitHub Copilot, Cursor, ChatGPT, Claude) are changing how code is written. But the difference between using them well and using them badly is enormous: a team that masters them is significantly more productive; A team that uses them without judgment introduces subtle bugs, technical debt, and ununderstanding dependency.
At Q2BSTUDIO we form development teams in the professional use of AI for programming. It's not a generic prompting course: it's training for developers who want to integrate these tools into their workflow while maintaining quality, security, and understanding of the code they produce.
We cover: how an LLM works at a conceptual level (without going into advanced ML, but enough to understand its limits), effective prompting for code (context, specificity, examples, constraints), use of Copilot/Cursor in the IDE (completions, chat, edits, agent mode), critical review of generated code (detect subtle errors, validate logic, verify security), and when NOT to use AI (architectural decisions without context, critical security, a domain that the model does not know).
We address production patterns: rapid scaffolding with AI + human refinement, generated and validated tests, assisted refactoring, automated documentation, debugging with context, and exploration of new APIs/libraries. We also cover anti-patterns: copying without understanding, accepting without revising, relying on the model for decisions that require business context, and ambiguous prompts that generate incorrect code.
Security is a transversal axis: what not to share with the model (secrets, personal data, proprietary code in cloud models), how to validate that the generated code does not introduce vulnerabilities, and when human review is mandatory vs optional.
The exercises are practical: participants use Copilot/Cursor on their own project (or a provided one) and compare productivity with and without AI, output quality, and review time. At the end, each person has criteria to decide when the AI accelerates and when it introduces risk.
We provide prompting guidance by use case, review checklist and suggested use policy for the company.
FEATURES
Features of AI Scheduling and Prompting
Fundamentals of LLMs for devs
How the model works, its limits and why it is sometimes hallucinating.
Effective prompting for code
Context, examples, constraints and formats that improve output.
Copilot/Cursor in the IDE
Completions, chat, edits, agent mode and optimal configuration.
Critical review of output
Detect logical errors, subtle bugs and vulnerabilities in generated code.
Production patterns
AI-assisted scaffolding, testing, refactoring, docs and debugging.
Security and privacy
What not to share, security validation and usage policies.
Anti-patterns
When NOT to use AI and common errors that introduce technical debt.
Comparative exercises
Same task with and without AI: measuring productivity, quality and review.
TECHNOLOGIES
- TypeScript
- Python
- OpenAI API
- GitHub Copilot
- Cursor
FREQUENTLY ASKED QUESTIONS
Frequently asked questions about AI Scheduling and Prompting
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