Synthesize the entire course into one end-to-end AI-assisted engineering workflow. Map shared context, planning, implementation, tooling, and maintenance into a curate → plan → build → validate delivery loop.
Instructor:Lesson nine is a capstone. Four exercises. One repo. If you're following along, get the GitHub example link from the section below. You've already seen the pieces: context, planning, implementation, guardrails, and maintenance. Now all you need is to run the loop end to end. Start with the context layer, with the docs and the repo map, and remove the guesswork. If the agent can explain the notification flow from docs, you already have the foundation right. Second thing, please don't worry if I'm running too fast on this slide. The detailed example documentation is available as Markdown in the GitHub repo. We'll walk through it toward the end of this slideshow on the codebase. So I'll show you everything we've set up, and what you can do to recreate it. Now for the planning phase. Before the code, the prompt must return a plan, not TypeScript.
Instructor:Test it with the right prompting. If it writes code, tighten the prompt. Next, let the planner write the spec, let the tester write the failing test, and let the implementer make it pass. The feature is audit logging for your preference changes. Now add the criteria: block the missing audit, block the new rules without tests, and then record your context inventory. So you can maintain it. That's the loop: curate, plan, build, and validate. Now, please get the GitHub example repo from the link. Star it. We appreciate that, and watch it for updates on GitHub. We will keep adding more and more examples for everyone's benefit. Now let's quickly walk through the GitHub code. We are in VS Code, and this is the example layout. Example nine has some context ready, and the documentation ready for you.
Instructor:There will be some missing links. That's the idea. You will find them and fix them. Not a lot. Exercise one is available here. What are you supposed to do? There is sufficient information, and the objective is given. Exercise two is given. Exercise three and exercise four. All those exercises are in sequential order. That means, you cannot jump between exercises. Getting exercise one right is the prerequisite for two. Getting two right is a prerequisite for three, and the same applies to number four. Your core objective is to get one to four right, without failing once. Once you get that, you have completed the loop on a real-world example, with real-world experience. How to move from setup to guardrails. That will internalize the workflow. It is possible you will not get it the first, or second, or even third time. The whole idea of this exercise is to keep repeating it. Even if you manage one to four in one go,
Instructor:do it a couple of times. The reason is, you will find better ways to do it again and again. And that's the whole idea of internalization. AI-assisted programming and engineering are essential now. Without that, everyone will struggle. So make sure you put enough effort and time into this base skill. This is pretty much a core skill. That will allow people to build further skill on top of it. Getting it right, with the correct understanding, is essential. And this will make sure it happens. So let's go back to the presentation and conclude the whole course. So we are going back to the presentation. GitHub repo, everything I have already described, and I thank you, and I want you to give us a star there as well.
Instructor:So that's the capstone. Get the GitHub repo, and get it on. Subscribe to LocalM Tuts, and I'll see you in some other course. Thank you.
Learning Objectives4
Synthesize all course layers into a single curate → plan → build → validate delivery loop
Map each customization surface to its corresponding SDLC phase
Design a minimum viable context stack for a real team starting from scratch
Apply the progressive complexity principle to sequence a team adoption plan
Q&A
Q & A
Q
Do I need all seven customization surfaces to be effective?
No. Most teams get substantial value from instructions and docs first. The rest are optional layers that should earn their complexity by solving real workflow problems.
Q
What is the capstone lesson trying to teach beyond feature inventory?
It teaches a delivery system. Context engineering is valuable because it improves the whole flow from planning through validation, not because it gives you a collection of isolated configuration files.
