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Challenge 3: Context-Powered Iteration

Recap:

In Challenge 2, you turned your app from a prototype into a product. Hardcoded mock data became real conditions from live data sources. You added pages, navigation, and polish, and everything was saved and synced so your whole team could see it.

Then in Lesson 3, you hit the wall: every new conversation meant re-explaining your entire project from scratch. You learned why AI forgets between conversations, and you solved it. Your team wrote a house-sitter note, turned it into a real project context file, and tested it. AI knew your project without being told.

You also practiced two ideas that matter right now:

  • Table of contents, not the whole book. Your context file is short and points to your project's documentation. It doesn't try to contain everything.
  • Start fresh for fresh eyes. When a conversation gets long, start a new one. With the context file in place, starting fresh costs you nothing. AI picks up right where you left off.

The Challenge

Your AI coding assistant knows your project now. Every conversation starts with context instead of confusion. That changes everything about how fast you can build.

In Challenge 1, you were learning how to talk to AI. In Challenge 2, you were learning how to work in a real project. In Challenge 3, you're just building, and context is the reason you can move this fast.

Time to be ambitious. Your Expedition Safety Brief has real NPS alerts and NWS weather data. Now push it further: USGS streamflow readings at key river crossings, safety recommendations that adapt to current alert levels, a pre-departure brief you could hand to a hiker before they leave the trailhead. The exact direction is up to your team. Context makes iteration fast enough to go wherever your ideas take you.

Build incrementally, one feature at a time, verified before you move on.

What to Build

Items are listed in priority order. If time is tight, focus on the items near the top first.

  • Your project context file is working. Start a fresh conversation and verify that AI knows your project, your data sources, and your design without you explaining anything
  • Streamflow data is part of the brief: real-time CFS readings from USGS gauges at Yellowstone river crossings, showing whether crossing conditions are safe (your repository already has this data; ask your AI coding assistant what streamflow information is available)
  • Condition-specific recommendations: the brief gives different safety advice depending on current alert levels (a Caution alert calls for very different guidance than a Danger alert)
  • At least one team-chosen feature: something your team is excited about, whether it's from the stretch goals below or an idea of your own

These are options for teams that finish the baseline capabilities. Your team can also define your own stretch goals based on what interests you.

  • Printable pre-departure brief: a summary page combining today's NPS alerts, NWS forecast, streamflow readings, and route recommendations into one view someone could screenshot before heading out
  • Adaptive gear checklist: packing recommendations that cover backcountry essentials and adapt to expected trail and weather conditions
  • Trend display: show how streamflow levels or alert counts have changed over recent days so hikers can see whether conditions are improving or deteriorating
  • Critical alerts: automatically surface the highest-priority NPS alerts first (Danger before Closure, Closure before Caution) so the most important information is never buried
  • Route planning helper: let the user pick a trailhead, zone, or river crossing and see tailored conditions and advice for their specific planned route

Tips

  • Start a fresh conversation first. Before you build anything, open a new conversation and make sure your context file is doing its job. Ask your AI coding assistant: "What is this project and what data do we have available?" If it knows, you're good to go. If it doesn't, fix the context file first. Everything else in this challenge goes faster when context is working.
  • Update your context file as you build. Tell your AI assistant to update the project context file when you: add a new data source (like USGS streamflow), add a new page or section, change how the app is organized, or make a design decision you want AI to follow consistently (like "always display NPS alert categories using their official names"). A quick "Update the context file to include what we just built" takes seconds and keeps future conversations sharp.
  • Try the discovery prompt. When you're ready to add streamflow data, try: "What USGS streamflow data do we have available in this project? Show me what's there and suggest how we could display river crossing conditions in the brief." Let AI explore for you.
  • Ground safety recommendations in real sources. When AI generates advice like "do not cross when flow exceeds 800 CFS at the Lamar River gauge," verify it against USGS historical data and NPS trail advisories for that station. AI can phrase things convincingly but get safety thresholds wrong. Use AI to build the feature, but verify the safety content against authoritative sources like the NPS Yellowstone trail conditions and the GYCC.
  • Save and sync often. You know the drill. This is your safety net. Save after every feature that works.