After class, the most feared is not being tired but losing everything. The typed transcript no one wants to read again, the presentation remains the same as before, and good questions asked in class are hard to find when preparing for next time. This article breaks down my own "Post-Class Organization Loop": how do I generate three different products from a batch of post-class materials using just six steps, and how do I review them at two levels? After reading this, you can create your own conveyor belt based on the process described here.
- They attend classes, give lectures, and lead workshops, leaving piles of notes and presentations at the end of each session, only to have them deteriorate over time.
- They want to use AI to help organize course content but are unsure how to set standards or verify that what the AI produces is usable.
- They possess a large amount of one-time materials (meetings, interviews, live streams), aiming to transform them into reusable knowledge assets.
- A six-step template: from storing original drafts to going live, each step's actions and deliverables.
- A routing strategy that turns one source into three deliverables, ready to apply to your own organizing workflow.
- The two-tier review method: which step uses the lighter, and which step upgrades to the heavier, as well as how I avoid putting gold on my face.
First, let's introduce a concept: One piece of material yields three different products
Many people believe that organizing after-class materials is just rearranging the script. In fact, for the same class session, I need to produce three different finished products:
- Teaching Manual: One for my own review and preparation for a future lesson.
- Post-Class Recording Briefing Version: Another for students' review purposes, filling in the on-the-spot content that was added during the class.
- Course Page on the Official Website: And an external one, converting live good questions into public content.
The key lies here: The same batch of materials can have three different exits from a single workflow, not by doing it three times separately. Once you understand this, each subsequent step makes more sense.
Trigger-based routing: different instructions lead to different scopes of work.
This is one of the most confusing and valuable points to teach about this Loop. When you feed in the same script, what I say determines how far it goes.
| What I say | How much it runs |
|---|---|
| "Post-Class Cleanup Loop" | Run all six steps and take it all the way to the official course page on the website. |
| "Cleanup Script / Create Teaching Manual" | Only do Step 2 (rewrite), stop at the teaching manual. |
In other words, when I say "organize the script," I'll only get a manual; when I say "post-class organization loop," I'll actually go all the way to the website. This is intentional scope definition that ensures I don't have to explain where we are at any point.
The first action before starting: First, inventory, don’t rush into action.
This step comes from blood and tears. Before diving into rewriting, I'll go through the course board and pull out all the existing content for this class: pre-class PowerPoint presentations, outline maps, pre-class survey results.
Six Steps, Break Down Gradually
The six steps below represent each segment of the conveyor belt. The first two steps transform written scripts into reusable materials, while the last three steps push these materials to an external website. The step labeled "2b" involves completing pre-class presentations into post-class versions.
Keep the original script as recorded and transcribed verbatim in its own folder for that class, with a date-time stamp in the filename. The only rule is: keep it unchanged, no additions allowed. It serves as the original evidence; all subsequent processed versions grow from it, so it must remain pristine, untouched by any alteration.
Organize the script into a structured lesson manual that retains my speaking tone. This is the most time-consuming and requires standardization step in the entire Loop. Three fixed sections are essential: 💬 Teacher's Speech, 📌 Key Points, 🙋 Student Questions. Include a complete list of student questions at the end.
If there was an on-the-spot presentation during this class (which is almost always the case), this step fills in content that wasn't included in the presentation. The method involves comparing the script with the presentation, adding "content spoken but not covered in the presentation" into a few projection cards for local updates. This preserves the original student connections.
Identify what from this class is worth sharing on the course page: good questions and answers asked during the live session, details that need to be supplemented, adjustments needed for the website's color scheme and font sizes for mobile compatibility. This step involves thinking through which parts to change before actually doing so; it’s about planning ahead rather than jumping in without a clear plan.
Once you're on the course page on the website, make sure to clean up all the necessary changes: the top brand navigation bar, the post-class Q&A section, and the color scheme and mobile layout. Make sure each of these areas is addressed; if any are missed, the page will look odd.
The last line of defense after customizing everything. After completing steps 1 through 4, by default this card should be placed in the website board for queueing (from Candidate to Layout in Review → Pending Deployment). Don't push it live yet; only when I explicitly say "deploy directly" or "deploy immediately" will it go live. Once deployed, you can’t take it back; adding an extra step of queuing gives you more opportunities for review.
How do I review it? Let's break it down into two layers.
This is where I want to explain the most clearly. People often ask me, "How do you ensure that what AI generates can be used?" This isn't something that can be explained in one sentence because I use two different levels of reviews applied at different stages. Let's separate them and avoid mixing them up.
First layer: Review during the draft stage (What we actually use after class notes are done)
During the process of reviewing transcribed scripts, my approach works like this:
First, I don't clean it directly in the main conversation.All course transcripts are processed by a separate sub-agent (typically using models like Opus without any downgrade), which handles transcription. The main conversation focuses on assigning tasks, collecting results, and moving to the next step. Why do we need an independent workspace? Because transcribed scripts can be very long; running them directly in the main conversation would push aside the context I need to handle later.
Second, I will independently reverify everything after it's returned.After the sub-agency proofreading is done, it doesn't end there; I'll take its results and compare them to the original line-by-line transcript for two things:
- Are any of the key phrases been rewritten?The vivid metaphors and unique expressions that I use in my lectures must be preserved exactly as they are, not altered by AI into formal written language.
- Is the text compressed too much?Have all the necessary details been cut out?
The key of this second-level review is that "the original manuscript is still in hand and can be reviewed at any time." This is why step 1 must first save a static original version, which serves as the baseline for comparison during re-review.
Second layer: Review (proofreading) stage of external output
I have another more rigorous review mechanism that includesCross-departmental proofreading(AI wrote the article, then had another AI review it; self-reviewing is not allowed),SSR Reader Self-Review,(To simulate how a target reader would go through the article once, checking if the title grabs attention, if the content flows well, and if there are practical takeaways for readers), It also includes five review stages.
The Three Design Approaches Behind This Loop
If you want to apply this set of steps in your own organizing process, what truly makes it valuable are these three approaches; they're more important than the six steps.
Keep one original draft version permanently unchanged, with a separate clean copy. This way, edited versions won't contaminate the original evidence, making it easier to verify later.
Think of materials as "a batch of content that is divided into different products based on different audiences," not just "producing one thing." The depth and tone required for self-use, training students, or public consumption are all different.
During the cleanup phase, let junior agents handle it (in case they mess up the original draft, you can start over); during the go-live phase, set up a queue for confirmation (because once something is released, it's hard to take back). The force should be proportional to the risk.
Common Pitfalls (Used as Negative Examples)
- ❌ Forgot to do a preliminary check: Found out after running through one round that the presentation was still from an old version before class.
- ❌ Placed the entire handbook inside the presentation：Slides should be concise summaries, not a direct transcription of the entire document.
- ❌ During revision, refine my metaphor into formal written language.The tone and personality are the soul of the class; they disappear when refined away.
- ❌ Only clean half of it, leaving out student questions.：Good questions from students often contain the most valuable parts.
- ❌ Automatically move unapproved content to the external version.<0000>: Things not yet online must be confirmed first.
Coach Jiang
<0000>Post-Class Organization Loop, at its core, is about converting "knowledge assets that can be continuously drawn upon in the future" by organizing and standardizing "the knowledge from a single class session" in an orderly manner.
<0000>It relies on three simple things: keeping the original clean, distributing one material to multiple outlets, and having more defenses as you go outward. Do these three things well, and AI is merely helping you scale up the volume.</0000>