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