Learn AI Literacy with Phoebe

Understand AI, decide with confidence

Eight 45-minute sessions - the sequel to data literacy - that take leaders and practitioners from neural nets to LLMs to AI strategy. What AI actually is, how it learns, embeddings, recommendations, search, how large language models work, using GenAI well, and the strategy and risk decisions that turn all of it into action. No code anywhere. An interactive AI toy in every session - be the neuron, feel the temperature, tune the ranking. You leave able to evaluate, decide, and govern AI, not just talk about it.

8sessions
8interactive AI toys
0lines of code
1sequel to data literacy
🪆 Session 1 · foundation

What AI actually is

AI vs ML vs deep learning vs GenAI as nested dolls, the three eras, and the right-tool test - is this even an AI problem?

▶ Start hereWhich-doll sorter 🧠 Session 2 · foundation

Neural nets and deep learning

A neuron is a weighted sum and a threshold; layers make a network; "deep" means many layers; how training adjusts weights.

be the neuron 🗺️ Session 3 · core

Embeddings: how machines read meaning

Words and images become vectors; similarity is distance in meaning-space; one embedding space powers search, recs and LLMs.

nearest-neighbour explorer 🎯 Session 4 · core

Recommendations

Collaborative vs content-based filtering, the Netflix and TikTok engines, cold start, and the filter-bubble failure modes.

rate-and-predict sandbox 🔎 Session 5 · core

Search and ranking

Keyword to semantic search, ranking as a weighted signal sum, why there is no objective #1, and a first look at RAG.

weight-the-signals tuner 💬 Session 6 · stretch

How LLMs work

Next-token prediction, tokens, context window, temperature, and why fluent is not the same as factual - the GenAI heart.

the GenAI heartnext-token predictor ✍️ Session 7 · stretch

Using GenAI well

Prompt patterns, RAG for grounding, the prompt vs RAG vs fine-tune decision, agents at intuition, and evals before trust.

weak vs strong prompt lab 🛡️ Session 8 · capstone

AI strategy and risk

Build / buy / API, the five-risk grid, real ROI vs hype, human-in-the-loop governance, and the use-case scorecard + memo.

the finaleuse-case scorecard
foundation - everyone core - the working toolkit stretch - deeper intuition capstone - decide and govern

Choose your path 🗺️

Everyone starts at Session 1 - the nested dolls and the right-tool test carry every other session.

🏛️ The curious leader 1→ 4→ 6→ 8→ what it is, where it shows up, the LLM heart, the decisions
🌱 The full journey (recommended) 1→ 2→ 3→ 4→ 5→ 6→ 7→ 8→ recommended
💬 Straight to GenAI 1→ 3→ 6→ 7→ the fastest line to prompting, RAG and using LLMs well

✓ What this course is

Understanding you can decide with: what AI, ML, deep learning and GenAI are, how neural nets and embeddings work, recommendations, search, how LLMs work, prompting and RAG, and AI strategy and risk. Everything a leader or practitioner needs to evaluate, decide, and govern AI.

✗ What it is not

No math (backprop, loss functions, linear algebra). No coding with APIs, no training your own models, no MLOps. This is literacy: understand, evaluate, decide, govern - not build. The builder track is a different course.

New to data itself? Start with the data literacy course first - it sits underneath everything here.

The knowledge map 🧠

The whole course at a glance - hover a session to spotlight its concepts, click any node to jump in.