Training Program · AI Prompting
AI Prompting Training Program
40 hours across three phases — prompt engineering fundamentals, AI-assisted software development, and responsible, governed AI adoption across the business.
Who It's For
Built Around Real AI Adoption Needs
Each phase builds toward a different level of AI fluency and responsibility.
Anyone Using AI Assistants Daily
Learn reusable prompting frameworks and how to evaluate AI outputs critically, vendor-tool agnostic.
Developers & Engineering Teams
Use AI coding copilots effectively across the SDLC — for code, refactoring, docs, tests, review, and automation.
Managers & Business Teams
Identify high-value use cases and apply AI responsibly, with clear team guidelines and governance.
Curriculum
What Each Phase Covers
Prompt Engineering Foundations
Reusable prompting frameworks and disciplined evaluation of AI output.
- How LLMs generate text, and prompting as an iterative skill
- Zero-shot vs few-shot prompting, role/persona assignment
- The Role-Task-Format framework and chain-of-thought prompting
- Systematic prompt testing, debugging, and context management (incl. RAG basics)
- Reusable prompt templates and a team prompt library
- Evaluating outputs: hallucination checks, bias testing, consistency, review checklists
- Capstone: design & evaluate a prompt library
AI in the Software Development Lifecycle
Using AI coding copilots effectively and critically across the SDLC.
- How AI coding copilots work: completion vs chat-based assistance
- Effective prompting for code generation, refactoring, and modernization
- Recognizing incorrect/insecure suggestions and over-reliance risks
- AI-assisted documentation and unit test generation, with human validation
- AI-augmented code review and bug diagnosis
- Chaining prompts into simple, monitored automations
- Capstone: AI-augmented development workflow
Applying AI Responsibly Across the Business
High-value business use cases balanced with ethics and governance.
- Practical use cases: drafting, summarization, research, incident/support workflows
- Judging good vs risky AI use cases and measuring success
- Data protection: what not to paste into AI tools, enterprise vs consumer tools
- Licensing, disclosure, and avoiding unattributed AI content
- Recognizing biased/harmful outputs and mandatory human-review thresholds
- Drafting lightweight team norms for responsible AI use
- Capstone: responsible AI use playbook
Hands-On & Frameworks
Tools & Frameworks Used Throughout
Capstone Projects
Each phase closes with a capstone — designing and evaluating a prompt library, building an AI-augmented development workflow, then producing a responsible AI use playbook — reviewed through prompt review, code walkthrough, and a use-case pitch.
The AI Angle
Why This Is the Skill Everyone Needs Now
This Track Addresses the Shift Directly.
Prompting well, integrating AI into the SDLC responsibly, and knowing where to trust — and not trust — an AI's output is quickly becoming a baseline expectation for IT roles, not a specialist add-on. This is the track built for that reality, head-on.
Outcomes
What Participants Can Do Afterward
Prompt with Precision
Master reusable prompt engineering frameworks and evaluate AI outputs critically for accuracy and bias.
Accelerate Development Responsibly
Use AI coding copilots effectively across the SDLC — without over-relying on unverified output.
Adopt AI Responsibly
Identify high-value use cases and apply AI ethically, with clear team guidelines and governance.