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 👇

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

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

Please share everything that you've learned in Module 4. Also review learnings from other students to make sure you are aware, and share your thoughts if you agree/disagree.

Task: Create Problem Statements from Empathy Stage Findings The designer has completed the Empathy Stage of the design thinking process for the food delivery app. Below, you will find the key user insights, user personas, and emotional map that summarize the findings. Your task as the Product Manager is to identify the pain points and create clear problem statements for each. These problem statements will help guide our ideation and prototyping phases, ensuring we address the most pressing user needs. The findings are shared below: 1. Key User Insights: Through interviews, surveys, and observation, we have gathered the following insights about our users: "I want my food to arrive on time and still be warm." (4 out of 5 users mentioned this) Insight: Users feel frustrated when their food arrives cold or late, which diminishes their overall satisfaction. "It’s hard to find the food I want quickly—there are too many steps." (3 out of 5 users mentioned this) Insight: Users find the app’s navigation complex and time-consuming, leading to frustration when they can’t easily find what they want. "I wish I could filter restaurants based on dietary needs like vegan or gluten-free." (2 out of 5 users mentioned this) Insight: Users feel limited by the current filtering options, which don’t accommodate specific dietary preferences. "I don’t trust the app’s delivery status updates; they aren’t reliable." (3 out of 5 users mentioned this) Insight: Users feel anxious and uncertain because they don’t receive accurate or timely updates on the status of their delivery. 2. User Personas: Persona 1: The Busy Professional Needs: Quick access to meals during work breaks, reliable delivery times. Frustration: Delays in delivery and difficulty navigating the app. Persona 2: The Health-Conscious User Needs: Easy access to healthy food options, the ability to filter restaurants by dietary needs. Frustration: Lack of dietary-specific filters and confusing app navigation.