Case Study

Capstone Project ĀKI

ĀKI was an IT7510 capstone (45 credits, Grade A) focused on delivering a web-based AI “virtual assistant” for timetable and course/IT query support. The work combined LLM system prompt engineering with disciplined reliability evaluation across multiple local runtimes, plus a security-minded testing loop - delivered with structured project management through to final submission.

LLM researchProject ManagementDelivery

What I led

As Project Manager, I led a team of 4 students to coordinate planning and delivery across all phases of the capstone: proposal/milestones, research compilation, documentation, demonstrations, and structured testing/verification. I owned coordination workflows to keep the team aligned and the work progressing through the full 16-week duration.

Project Manager4 students

Key deliverables

  • Final approved project proposal, objectives, and milestone plan (Agile/Kanban delivery)
  • Project timeline and milestones documentation (including Gantt/milestone planning)
  • Research compilation documenting open-source LLM hosting environments and tooling experiments
  • System prompt/timetable documentation and evaluation assets used during demonstrations
  • Demo materials (video walkthroughs) showing AI behaviour across different execution platforms
  • Testing/verification documentation (UAT + unit testing + security testing evidence)
  • Repository deliverables and final submission documentation for capstone completion

Methodology & tools

  • Agile methodology with Kanban workflow to manage discovery, development, testing, and iteration
  • Jira for task tracking and sprint/milestone coordination
  • Git/GitHub for version control and collaborative development
  • Weekly planning and progress communication to keep the team aligned (e.g., Discord/messenger)
  • System prompt engineering workflow: defining prompt rules, iterating, and validating behaviour
  • Web-based chatbot delivery connected to local model environments for consistent testing/benchmarking
  • Structured documentation habits to support reproducibility and final submission quality

Testing & security focus

  • System prompt injection and behavioural testing (conflicting prompts and adversarial-style inputs)
  • Checks for prompt leakage / system prompt disclosure risks
  • Security testing evidence as part of iterative verification (UAT + unit testing)
  • Evaluation across multiple runtime environments to compare reliability and output characteristics
  • Security hardening concepts such as input validation, rate limiting, and security headers (as part of verification)

Key skills

Project management + coordination (Agile/Kanban, Jira, delivery milestones)AI system prompt engineering and behaviour evaluationWeb-based interface work + integration with local model environmentsTesting discipline (unit + UAT + verification documentation)Security-minded assessment of AI behaviour under adversarial-style inputsTechnical communication: converting complex work into clear deliverables and demos

What this shows

This capstone demonstrates strengths: structured project leadership, the ability to translate academic research into practical engineering outputs, and a security-minded approach to evaluating AI behaviour.

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