Host: Welcome back. You're in GenAI Architecture and Design Patterns — a course about how to structure production artificial intelligence systems so they don't fall apart under real-world load. This chapter focuses on something called an ADR engine. ADR stands for Architecture Decision Record — a short written document that captures a single important technical choice your team made and the reasoning behind it. Today's skill matters because decisions without paper trails become someone else's problem six months later.
Expert: Let me paint the picture. Imagine you're on a platform team at a mid-sized company — maybe two hundred engineers, a dozen teams shipping AI features. Six months ago, someone decided to use a particular large language model for your customer support assistant. Nobody remembers why. The person who made that call left. Now a new team wants to use a different model for a similar use case, and there's no record of the trade-offs that were weighed the first time. Worse, the original decision was never formally reviewed — it just happened in a chat thread. When regulators or auditors come asking how your company governs its AI choices, you have nothing to show them. This is the exact situation Architecture Decision Records are designed to prevent. But writing the records isn't enough. You need a system that makes sure each record gets reviewed by the right people, gets formally approved, and gets revisited before it goes stale. That's what you'll build today.
Host: So this is the second exercise in a three-part sequence. In the previous one, you built a coverage report — a tool that scans your services and flags which ones are missing their required decision records. Now you're extending that work. So what are we building in this exercise?
Expert: You're building a review workflow. Think of it like the approval process for a pull request on code, but applied to architecture decisions. When someone drafts a new decision record, your system routes it through a series of checkpoints before it becomes official. The first checkpoint is reviewer validation — making sure the people assigned to review are actually qualified for this type of decision. A decision about model hosting, for instance, should be reviewed by someone with infrastructure expertise, not just anyone on the team. The second checkpoint is the approval gate — a formal sign-off where reviewers either accept the decision, reject it, or send it back for revision. And the third piece, which is really the heart of this exercise, is expiry tracking. Here's the key idea: different kinds of architecture decisions age at different rates. A decision about which cloud region to host in might stay valid for two years. A decision about which specific language model to use might need a review every six months, because the model landscape changes so fast. A decision about whether to use retrieval-augmented generation versus fine-tuning might need an annual review. Your system needs to understand these different shelf lives and flag decisions before they expire — not after.
Host: That's a lot of moving pieces. Before someone starts coding — what's the part that trips people up?
Expert: The trickiest part is handling the expiry logic cleanly. People's first instinct is to write one big chunk of code that checks every decision record with a pile of if-then branches — if the decision is about model selection, use six months; if it's about hosting, use two years; and so on. That works for three categories. It falls apart when you have fifteen. What you want instead is a small lookup table that maps each category of decision to its expiry window, and a single piece of logic that reads from that table. That way, when your team adds a new category next quarter, you change one line of configuration instead of rewriting the review engine. Same principle applies to reviewer validation — don't hard-code who reviews what. Use a mapping that says "this category requires reviewers with this expertise tag." Keep the rules as data, and keep the engine that applies the rules simple and general. The other thing to watch for: time zones. When you're comparing today's date against an expiry date, make sure both dates are in the same reference frame. A decision record reviewed from an office in one country and checked from a server in another can produce off-by-one-day surprises if you're sloppy about this.
Host: Good warning. So wrap it up — what does the listener walk away able to do, and what comes after this?
Expert: After this exercise, you'll be able to take a stream of architecture decisions and run them through a complete review lifecycle — validating who's allowed to approve what, enforcing formal sign-off, and automatically surfacing decisions that have aged out of relevance. This is a genuinely reusable building block. Your team can drop it into any governance process where decisions need to be formally tracked and periodically revisited — not just AI decisions, though that's where it shines. In the next and final exercise of this sequence, you'll combine this review data with the coverage report you built earlier to produce an executive-level compliance report — the kind of single-page summary a chief technology officer or a board committee would actually read. So you're building toward a complete governance picture: what's documented, what's reviewed, and how healthy the whole system is. Thanks for listening, and good luck with the build.
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