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 we have 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 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 as part of the learning summary. (I recommend using Google Nano Banana for it) Finally, please share: - How would you define "good quality" in this case - How would you measure success of the "Online classes learning summariser" feature

Hi everyone! It’s great to connect with you all through this community. I’m currently transitioning into an AI Product Manager role and would love to build meaningful connections here. Looking forward to engaging and learning together!