Host: Welcome back. You're in GenAI Architecture and Design Patterns — a course about making the big structural choices behind real AI systems. This chapter is about something called an ADR Engine. ADR stands for Architecture Decision Record — basically, a written note explaining why your team picked one technical approach over another. And this specific skill — checking whether those written decisions are still true in production — is something every architecture team wrestles with the moment their system leaves the whiteboard.
Expert: Right, and let me paint the picture before we go further. Imagine you're on a platform team at a mid-sized company — maybe eighty engineers, a handful of AI products in production. Six months ago, your architects sat in a room and made a decision. They wrote it down. It said something like: "We will use a smaller, cheaper language model for customer support replies because our average question is short and simple, and the cheaper model answers correctly ninety percent of the time." That written note is an Architecture Decision Record, or ADR. It's the team's memory of why things are the way they are. Now fast forward six months. Customer questions have gotten longer. The cheap model is now only correct seventy percent of the time. Complaints are climbing. But here's the thing — nobody noticed the original assumption broke. The decision document is sitting in a wiki, and production reality has quietly drifted away from it. That gap — between what your team decided on paper and what the metrics actually show — is where bad AI systems come from. Your job in this exercise is to build the thing that catches that drift automatically.
Host: Perfect setup. This is the first exercise in the chapter, so there's no prior lab to build on — the chapter overview walked through the big picture of decision records, and now we're getting specific. So tell me, what exactly are we building here?
Expert: You're building what I'd call a decision checker. It does two jobs. First, it reads through a decision record — remember, that's the written note explaining an architecture choice — and it pulls out the testable claims inside it. Things like "this model answers correctly ninety percent of the time" or "average response time will stay under two seconds" or "we'll spend less than five hundred dollars a month on this." Those are the promises the document makes. Second, your checker compares each of those promises against the actual numbers coming out of the running system — the production metrics — and tells you which promises are still holding and which ones have quietly broken. Now here's the key idea, the conceptual aha moment. Most teams treat decision records as paperwork — something you write once and forget. What you're doing in this exercise is treating them as living contracts. Think of it like a rental agreement. Signing it is nothing. What matters is whether both sides are still keeping their end of the deal months later. Your framework turns a static document into something that gets continuously checked against reality. Every claim in the record becomes a test that either passes or fails based on what the metrics say today.
Host: That framing really helps. So before someone sits down to code this, what's the one thing that's going to trip them up?
Expert: The tricky part is the extraction step — pulling testable claims out of human-written text. Decision records are written in natural language. A human reads "we expect ninety percent accuracy" and understands it instantly. But to check it automatically, you need to convert that sentence into something machine-readable: which metric are we measuring, what's the threshold, what's the comparison — greater than, less than, equal to. Students often underestimate this and try to handle every possible way a human might phrase a claim. Don't. The tip here: define a small, strict vocabulary of claim shapes your framework understands — something like "this named metric must be above this number" or "this named metric must stay below this number." Then, when you read a decision record, only extract claims that fit those shapes. Anything fuzzy or ambiguous, you flag for a human to rewrite. That keeps your validation honest. A framework that pretends to check vague claims is worse than one that refuses to check them, because it gives false confidence. Start narrow, get it working, then expand the vocabulary later.
Host: Solid advice. Let's close it out — what will the listener actually be able to do after this, and where does it go next?
Expert: After this exercise, you'll be able to take any architecture decision record your team has written and produce a clear report showing which of its assumptions still hold in production and which have broken. That's a concrete capability — not a concept, a working tool. For your team, this becomes the foundation of what some people call living architecture: a setup where your team's decisions are continuously checked against reality, so you find out an assumption broke in days, not in a postmortem after an outage. This is exactly the kind of production-grade thinking that separates teams who ship AI systems from teams who demo them. And it flows directly into the next exercise. Right now, your framework checks assumptions at a single point in time — a snapshot. In the next exercise, you'll extend it to watch assumptions over time and detect what's called drift — the slow slide where a metric doesn't break suddenly, it just gradually wanders away from what the record promised. You'll also wire it up to generate alerts in a format that a standard alerting system can read, so your on-call engineers get pinged the moment a decision goes stale. But that's next. For now, focus on getting the single-snapshot check rock solid.
Host: Great stuff. Go build it, and we'll see you in the next one. Thanks for listening.
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