Generative AI engineering practice

Project overview

Structured learning and hands-on practice in AI-assisted development, LLM tool calling, retrieval-augmented generation, MCP, agents, APIs and grounded document workflows.

Problem

AI concepts are easy to describe but harder to apply responsibly. My objective is to build practical, testable capability that complements business analysis—understanding when AI is useful, how it connects to tools and data, and where human review remains essential.

My role

I am building practical AI capability through Outskill's Generative AI Engineering programme. I complete hands-on exercises, apply the methods in product and website work, test limitations and document what would be needed for reliable production use.

Delivery approach

1. 01Learn each concept through a practical exercise rather than relying only on theory
2. 02Connect model behaviour to APIs, tools, context and grounded source material
3. 03Apply AI-assisted development methods in live web and product projects
4. 04Review outputs, identify failure modes and separate model-generated work from my own decisions
5. 05Document what is working, what remains experimental and what would require stronger production controls

What I produced

• Hands-on exercises and prototypes around LLM tool calling and APIs
• Practice with retrieval-augmented generation and grounded document workflows
• Agent and MCP exercises focused on connecting models with tools and context
• AI-assisted development workflows used across practical web and app projects

Evidence & business value

This work is developing my ability to move faster from a business requirement into a working prototype, assess limitations and communicate where AI is—or is not—the right solution.

• Structured Outskill learning supported by hands-on exercises
• Methods applied in live portfolio, website and product work
• Capability is clearly labelled as active development rather than production engineering experience

Technology & working methods

What I learned

A credible AI portfolio needs working evidence, grounded outputs, clear testing and an honest account of what I built, what the model produced and what remains limited.