Engineering design docs and API contracts decide whether your requirements survive implementation — and they are written for engineers. This playbook uses AI to translate designs into analyst terms, map them against your requirements, and generate informed questions, under one rule: never sign off on anything you cannot restate in your own words.
Workflow area: Solution Evaluation · Tier: Core · Hands-on time: ~30 minutes
The design review is where analysts traditionally go quiet. The sequence diagram flashes past, someone says "we handle that asynchronously via the event bus," heads nod, and three months later the business discovers that "asynchronously" meant confirmation emails can arrive before validation completes. Nobody lied — the analyst just approved a document they could not read. AI removes the excuse: any design doc can now be explained at whatever level you need, interrogated against your requirements, and mined for the questions that make engineers say "good catch."
Collect the design artifacts and your requirements. Get the actual documents — design doc, API spec, sequence diagrams, ADRs — not a summary from the tech lead. Pair them with the requirements and acceptance criteria this design is supposed to implement. The pairing is what makes the prep analytical instead of educational.
Get the plain-language explanation, layered. Attach the design doc:
You are helping a business analyst prepare for a design
review. Attached: the design doc and our requirements pack.
I am not an engineer. Explain the design in three layers:
1. ONE PARAGRAPH — what this design does, in business terms.
2. THE MOVING PARTS — each component: what it does, what it
talks to, and the business events it handles, avoiding
jargon or defining it inline on first use.
3. THE DECISIONS — every design decision with business-
visible consequences (timing, ordering, failure behavior,
data location, retention), each as: decision → what the
business will observe → the trade-off being made.
Cite the doc section for every claim. If the doc is silent
or ambiguous on something, say NOT STATED — do not fill in
how systems like this usually work.
Run the requirements-vs-design mapping. Second pass, same session: "For each requirement in the pack, state where the design addresses it (cite section), how the design's behavior could differ from the acceptance criterion, or NOT ADDRESSED." The interesting rows are never the clean fits:
REQ-018 (order confirmation within 2 min): design §4.2 uses
async processing with retry; happy path ~seconds, but retry
policy allows up to 15 min on downstream failure. Criterion
says 2 min unconditionally. DIVERGENCE — clarify whether the
SLA holds during downstream degradation.
REQ-023 (audit trail of price overrides): NOT ADDRESSED —
no logging of override events found in the design.
Generate questions, then filter them yourself. Ask for review questions a business analyst should raise, grouped by requirement risk, edge cases and failure behavior, and data lifecycle. Then cut anything you do not understand well enough to handle the answer — asking a question whose answer you cannot parse is worse than silence. Six good questions you own beat twenty you borrowed.
Apply the restatement test. For every element you will approve, write one sentence in your own words without looking at the AI output: what happens, when, and what the business sees when it fails. Where you cannot, that element goes on the review agenda — the inability to restate is the finding. This test is the playbook's spine; the AI exists to get you to the point where you pass it.
In the review, verify rather than perform. Lead with the divergences and NOT ADDRESSED rows from step 3. Restate answers back: "So when the downstream is degraded, confirmations can take fifteen minutes and the 2-minute SLA doesn't hold — is that what the business is accepting?" Log what was confirmed, what changed, and what the design team took away. Your sign-off note lists what you verified — which is what makes it worth something.
Design docs and API specs expose your organization's internal architecture and security posture — process them only in the approved AI environment, and treat repo-connected AI chat as having the same access sensitivity as the repository itself.
AI can explain any design at any depth, but the sign-off standard is unchanged and unautomatable — if you cannot restate what the system does, when it does it, and what the business sees when it fails, you are not approving the design; you are decorating it.