Generative AI
AI tools & creative workflow portfolio
Marisa D'Amore's growing collection of hands-on generative AI projects for research, writing, content strategy, design, audio, video and communication workflows, documented publicly on GitHub.
- Active
- 11 Aug 2026
- Prompt design, AI-assisted research, content strategy and human quality control
- Prompt design
- AI-assisted research
- Content strategy
- Multimodal workflows
- Human quality control
What this portfolio is
This repository is the central index for Marisa D’Amore’s practical work with generative AI and creative technology. It connects individual project repositories so that the work can be inspected rather than reduced to a list of tools on a résumé.
The collection currently includes experiments and documented workflows using ChatGPT, Google Gemini, Perplexity, Claude, NotebookLM, Microsoft Copilot, Google Pomelli, Canva, ElevenLabs, Suno and other creative AI tools.
Skills demonstrated
- Prompt design and refinement
- AI-assisted research and source evaluation
- Comparative review of model responses
- Fact-checking and human quality control
- Content strategy and content repurposing
- Image, audio and video production workflows
- Organising project evidence with GitHub and Markdown
- Learning new tools and documenting the process
How to read the work
The main repository is a portfolio index, not a single software product. Its evidence lives in the linked project repositories. Those projects show the objective, the tool selected, the resulting artifacts and, as the portfolio develops, the human decisions and lessons behind the output.
One example is the Pomelli campaign project, which turns an AI-assisted brand analysis into a structured seasonal marketing concept and a set of visual deliverables. Other repositories explore research, writing, multimedia and communication tasks across different AI platforms.
Marisa’s role
Marisa selects the tools and project goals, develops and refines prompts, reviews outputs, organises the deliverables and documents what each workflow produced. The portfolio is intended to show practical AI literacy and creative workflow development-not to present tool familiarity as machine-learning or software-engineering experience.
Status and limitations
This is an active learning portfolio. The strongest entries include concrete outputs, while others are still being expanded with fuller case studies. The next stage is to add more detail about source material, prompt iterations, quality checks, rejected outputs and lessons learned so that each project shows not only what the AI produced, but the human judgment applied to it.