🚀 Key Artifacts
- Learning Coach Prompt – Early prompt experiments
- Learning Coach Workflow – Step-by-step workflow design
- Week 1 Reflection – Insights, failures, next steps
This is a living lab for exploring applied AI systems through experiments, reflections, and iterative design.
This repository is a demonstration of my thinking, building, and learning velocity.
- assistant/ – AI assistant experiments, including prompts, workflows, and examples.
- experiments/ – Documented learning, failed approaches, and hypotheses.
- docs/ – System and architecture notes.
- reflections/ – Weekly reflections capturing insights and decisions.
I explore AI not as a black box, but as a system to reason with, shape, and understand.
I prioritize:
- Learning by doing: Rapid prototyping, testing, and iteration.
- Documenting the why: Every design, prompt, and workflow has rationale.
- Failing smartly: Dead ends are recorded as signals, not mistakes.
- Reflecting often: Weekly reflections improve both product and process.
- Framing AI as a “coach” rather than an “answer engine” increases engagement and reasoning depth.
- Role constraints in prompts change output patterns subtly but meaningfully.
- Iterative experimentation surfaces assumptions faster than isolated coding or reading papers.
- Expand prompt experiments with varied cognitive strategies.
- Build workflow-level experiments to test assistant reasoning.
- Continue weekly reflections, documenting trade-offs, failures, and insights.