This week you've got 5 activities that put everything from Module 2 into practice: 1. Fix the Prompt — Take broken prompts and rewrite them using the 5 Elements framework 2. Diagnose the Failure — Figure out why an AI product is giving bad output (hint: it's almost never the model) 3. Design the Context — Map out all 6 context components for a real product scenario 4. Classify the Approach — Decide whether a feature needs a simple prompt, RAG, an agent, or fine-tuning 5. Write a System Prompt — Write a production-quality system prompt from a product brief, then test it live This Google Doc has all 5 activities. Here's what to do: → Make a copy of the doc → Work through the activities → Link your completed copy as a comment on this post

Before you build AI, you define what "right" looks like. That's a golden set. Your task: Create 10 test cases for a travel itinerary chatbot. Define the user, their message, and exactly what the AI should (and shouldn't) do. Full brief with product context and template linked in the above post. Drop your submission link in the comments 👇

Please share a document with the LLM's name, prompt and the learning summary of session. Please include a visual (optional) Also share in the comments below how would you define "good quality" in this case, and how would you measure success of the "Online classes learning summariser" feature

Look at a product of your choice and apply the AI PM lens to it.

For the idea that you thought of in the Week 2 activity, share the following: Deliverable #1: Share the scores on each dimension and share a short description of why you rated it like that. Deliverable #2: Share the total score (Total Score = add all three) Deliverable #3 : What does your score tell you about your idea?