Granite / Automated Code Generators
- Duration: 7 hours
- Exam Voucher: Yes
- Language: English
- Course Delivery : E - Learning Access
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Course Overview
The Next-Gen Code Intelligence: AI-Assisted Software Development & Auto-Coding Tools course is designed to help developers and engineering teams operationalize AI code assistants (including automated code generators such as “Granite”-style models) across the software delivery lifecycle.It focuses on practical engineering workflows—requirements-to-code, refactoring, test generation, documentation, code reviews, and debugging—while reinforcing an important truth: AI can write code fast, but engineering accountability keeps it correct, secure, and maintainable.
Course Details
- Accelerate delivery: Use AI to draft features faster and reduce boilerplate effort
- Improve code quality: Apply AI-assisted refactoring, code review suggestions, and consistency patterns
- Boost test coverage: Generate unit tests and edge-case checks more efficiently
- Reduce debugging time: Use AI to triage errors, reason about logs, and propose fixes
- Standardize engineering workflows: Build reusable prompt templates and code-generation playbooks
- Responsible adoption: Add governance guardrails for security, privacy, licensing, and correctness
- Open to: developers, tech leads, QA engineers, DevOps engineers, engineering managers, and product engineers
- Recommended: basic programming experience and familiarity with standard SDLC practices
- Helpful (not mandatory): experience with Git workflows, code reviews, and test automation
- No mandatory prerequisites required
- Identify where AI code assistants add value (and where human review is non-negotiable)
- Convert requirements into structured prompts that produce usable, maintainable code
- Use AI for refactoring, documentation, and code comprehension
- Generate tests and improve reliability through structured validation workflows
- Apply AI-assisted debugging and root-cause reasoning techniques
- Implement responsible usage practices (security checks, data handling, licensing awareness, review gates)
- Introduction to AI code intelligence and modern auto-coding workflows
- Prompting patterns for developers: constraints, structured outputs, and reusable templates
- AI-assisted feature scaffolding: APIs, components, boilerplate, and integration-ready code
- Refactoring and code modernization with AI support
- Test generation: unit tests, edge cases, and regression mindset
- Debugging assistance: interpreting errors, logs, stack traces, and proposing fixes
- Code review augmentation: style consistency, performance suggestions, and maintainability
- Governance & responsible use: security hygiene, IP/licensing awareness, privacy boundaries, human-in-the-loop QA
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