Personal AI Fluency Plan
Overview
This document outlines a structured plan to develop AI fluency over the next 12 months. The plan is based on the AI Fluency Framework from Anthropic and is tailored to a beginner‑to‑intermediate learner.
Goals
- Understand core concepts of AI, machine learning, and large language models.
- Build practical skills in data preprocessing, model training, and deployment.
- Develop critical thinking about AI ethics, safety, and societal impact.
- Create a portfolio of projects demonstrating end‑to‑end AI solutions.
Timeline (12 months)
| Month | Focus | Deliverable |
|---|---|---|
| 1‑2 | Foundations | Complete AI Fluency Foundations course; write a summary. |
| 3‑4 | Data & ML Basics | Build a simple regression model on a public dataset. |
| 5‑6 | NLP & LLMs | Fine‑tune a small transformer on a custom text corpus. |
| 7‑8 | Prompt Engineering | Design a prompt library for common tasks; document best practices. |
| 9‑10 | Deployment | Deploy a chatbot using LangChain and Streamlit. |
| 11 | Ethics & Safety | Write a short essay on AI safety principles. |
| 12 | Portfolio & Reflection | Publish a GitHub repo with all projects; reflect on learning. |
Resources
- Courses: AI Fluency Foundations, Coursera ML, Fast.ai NLP.
- Tools: Python, PyTorch, Hugging Face, LangChain, Streamlit.
- Reading: The Alignment Problem, AI Ethics by Bostrom.
Milestones
- M1: Course completion + summary.
- M2: Regression model + GitHub repo.
- M3: Fine‑tuned transformer + demo.
- M4: Prompt library + documentation.
- M5: Deployed chatbot.
- M6: Ethics essay + final portfolio.
Reflection
After each milestone, review progress, identify gaps, and adjust the next steps accordingly.
Prepared by: [Your Name]