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

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

Submit your problem mapping here. 👇 How to Submit 1. Fill out the template from the essay 2. Post your response in the comments below 3. Read at least 2 other people's ideas and leave thoughtful feedback. Let's think this through. 👇

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