Treating post-project AI debriefs and decision records as compounding engineering assets
The Compounding Asset Most Engineers Throw Away
Every time you finish a project and close the last PR, something valuable gets deleted. Not the code. Not the tests. The reasoning. The “why we did it this way” that lived inside your head, inside a thread of Slack messages, inside a context window that got cleared the moment you moved on.
I’ve been building with AI tools seriously for about two years now. And the mistake I see most often, including ones I made myself, is treating AI sessions like scratch paper. You get the output, you ship it, you move on. The context dissolves.
That’s a terrible habit.
The RAM Model Is Wrong
Most engineers think about AI context the way they think about working memory. Temporary. Task-scoped. Flush it when you’re done.
The problem with that model is that it treats every new project as a cold start. You lose all the accumulated knowledge of what worked, what didn’t, and more importantly, why.
Andrej Karpathy recently wrote about a different approach he’s been experimenting with. He uses LLMs to build persistent personal knowledge bases stored as markdown files in a structured wiki. Raw source documents come in, the LLM compiles summaries and concept articles, backlinks get maintained automatically, and his own queries against the wiki get filed back in to make it richer over time. His words: “my own explorations and queries always ‘add up’ in the knowledge base.” https://x.com/karpathy/status/1908784031775412380
That phrase stuck with me. Add up. That’s compounding. That’s an asset.
What a Decision Record Actually Contains
A decision record isn’t a summary of what you built. It’s a capture of the thinking that won’t survive in any artifact.
Why did I choose a particular embedding model over the alternatives? What chunking strategy did I test first, and what specifically broke it in production? What did the benchmarks say, and where did the benchmarks lie?
That reasoning is perishable. It lives in your head for about three weeks after the project, then it’s gone. And the next time a similar problem comes up, you or someone on your team starts from scratch and makes the same wrong turn.
The fix isn’t complicated. It’s just discipline. At the end of a meaningful technical session with an AI tool, you ask for a debrief. You capture the decisions, the alternatives considered, the failure modes encountered, and the reasoning behind the final call. You put it somewhere that doesn’t require a specific Slack search to find.
Why This Compounds
Individual decision records are useful. A collection of them is something else entirely.
If you maintain this practice across projects, you start building a corpus of your own technical judgment. And that corpus becomes reusable context for future AI sessions. Instead of explaining your constraints from scratch every time, you hand the model your past decisions and it already knows how you think, what you’ve already tried, and what constraints are non-negotiable for your environment.
Karpathy’s setup gets interesting at scale. Once his research wiki hits around 100 articles and 400,000 words, he can ask complex questions against it and get answers that reflect his own accumulated understanding, not just generic retrieval. He notes he expected to need complex RAG infrastructure but found that well-maintained index files and summaries handled it at this scale.
That’s the difference between a tool and a system.
The Organizational Case
This matters even more on teams. Right now, institutional knowledge about technical decisions lives in the brains of whoever happened to be in the room. When those people leave or move to different projects, the reasoning leaves with them.
Decision records turn individual judgment into shared infrastructure. A new engineer inheriting a codebase can query the decision history and understand not just what was built but why the alternatives were rejected. That’s not a minor convenience. It’s the difference between maintaining a system intelligently and maintaining it by guessing.
Start Small, Start Now
You don’t need a Karpathy-scale wiki to begin. You need a directory, a consistent format, and the habit of running a five-minute debrief at the end of significant sessions.
The format I use: what problem we were solving, what options were considered, what we chose and why, what failed during the process, and what I’d do differently. Markdown. Dated. Filed by project.
Six months from now, that collection will be worth more to you than most of the code you wrote. Code gets refactored. Reasoning is what you can’t reconstruct.
The most durable thing you build with AI might not be the application. It might be the record of how you built it.
Sources
#AIEngineering #MachineLearning #SoftwareEngineering #KnowledgeManagement #TechLeadership
