AI Policy Making Day 7: Getting Down to Business (... oh wait ...)

Urban Planning, The 28 Day Plan, Resource Management, AInick williamson19 June 2023Agile, Kaipara, Innovation, Policy Development, Process DesignComment

AI Policy Making: Week 1 Summary

AI, Resource Management, The 28 Day Plan, Urban Planningnick williamson19 June 2023Agile, Kaipara, Innovation, Policy Development, Process DesignComment

AI Policy Making Day 6: Strategic Direction Completed

Many words were written today, which means a draft of the Strategic Direction Chapter is now complete. We’ve figured out how to keep ChatGPT on task when it begins to stray, and can now see how the District Plan manages and integrates all the issues. It’s a long read, so unless you are a prompting nerd, head over to the Prototypes page to see the Strategy Direction in context.

AI Policy Making [Intermission]: Friday Night Fun

After a chance meeting in a junkyard, "WALL-E," "Number 5," "Johnny 5," "Baymax," "R2-D2," and "Bender" become fast friends and decide to set up a secret hangout in an abandoned basement …

AI, The 28 Day Plannick williamson16 June 2023Just for FunComment

AI Policy Making Day 5: Back on Track (and in the flow)

After seeming to get slightly confused between the Alternative Bootleg District Plan we’re making and the official one the Kaipara District Council is presently working on, we got back on track by copy / pasting ChatGPT’s previous outputs back into the conversation as a reminder. At the end of today’s conversation we had a chat about how the Strategic Direction Chapter will help integrate the regulatory methods into the broader local government framework.

AI Policy Making Day 4: Quality Control Check ... But Things Get Wobbly

In the interests of quality control, we thought we would test whether ChatGPT understood what we were doing and recall the steps we had taken so far … it kind of worked … sort of.

AI Policy Making Day 3: Populating the Content

Once ChatGPT had set out the content summary for every sub-topic, populating the content was a very rapid exercise. Starting with the Introduction and Strategic Direction Chapters obviously helps, as the content is usually less specific and usually fairly well described for most Councils through the LGA processes such as the Long Term Plans (LTPs). Not having too many sources to draw from kept the AI more on track.

AI Policy Making Day 2: Content Outline Completed - (but watch out for distractions!)

Already we have developed a full outline of the entire district plan, including Chapters, Topics, and content outline for each subtopic. The content outlines include all matters required to be considered by higher order planning documents and the RMA.

Interestingly it places a much greater emphasis on things like community resilience, and economic development than is typically seen in district plans. Check out the hazards and economic development chapters in particular.

AI Policy Making Day 1: Filling in the 'Gaps' at National Policy Level

Day 1 of 28: Like all complex design projects, setting the scope, structure, and outline is a critical first step. Today’s focus was on developing the Table of Contents. If you get this wrong, your project will be doomed from the start.

The 28 Day District Planning Challenge: Day Zero

Welcome to the 28 Day District Planning Challenge! This is a challenge that we have created to help you get started on your district planning journey. Over the next 28 days, we will be sharing with you some of our best tips and tricks for district planning. We will be covering everything from how to create a district plan, to how to engage with your community, and everything in between.

Accelerating Policy Development with AI: A District Planning Case Study

Fluid Industries used AI to accelerate policy development in a district planning case study. The company used AI to analyze data and identify patterns that could help them make better decisions. They also used AI to create predictive models that could help them anticipate future trends and make more informed decisions. Read the full article to learn more about how they did this and what the results were.