Oct 09, 2025·8 min
How AI Turns Vague Prompts into Production-Ready Architectures
See how AI can turn vague prompts into production-ready architectures: framing requirements, surfacing assumptions, mapping tradeoffs, and validating designs.
See how AI can turn vague prompts into production-ready architectures: framing requirements, surfacing assumptions, mapping tradeoffs, and validating designs.
A “vague prompt” is the normal starting point because most ideas begin as intent, not a spec: “Build a customer portal,” “Add AI search,” or “Stream events in real time.” People know the outcome they want, but not yet the boundaries, risks, or engineering choices that make it feasible.
“Prompt to architecture” is the workflow of turning that intent into a coherent plan: what to build, how the pieces fit, where data flows, and what must be true for it to work in production.
Production-ready isn’t “has diagrams.” It means the design explicitly addresses:
AI is strong at accelerating early thinking: generating candidate architectures, suggesting common patterns (queues, caches, service boundaries), surfacing missing non-functional requirements, and drafting interface contracts or checklists.
AI can mislead when it sounds confident about specifics it can’t verify: picking technologies without context, underestimating operational complexity, or skipping constraints that only your org knows (compliance, existing platforms, team skills). Treat outputs as proposals to challenge, not answers to accept.
This post covers a practical, repeatable workflow for moving from prompt → requirements → assumptions → options → decisions, with tradeoffs you can trace.
It won’t replace domain expertise, detailed sizing, or a security review—and it won’t pretend there’s a single “correct” architecture for every prompt.
A vague prompt usually mixes goals (“build a dashboard”), solutions (“use microservices”), and opinions (“make it fast”). Before you sketch components, you need a problem statement that’s specific enough to test and argue about.
Write one or two sentences that name the primary user, the job they’re trying to do, and the urgency.
Example: “Customer support managers need a single view of open tickets and SLA risk so they can prioritize work daily and reduce missed SLAs this quarter.”
If the prompt doesn’t identify a real user, ask for one. If it doesn’t say why it matters now, you can’t rank tradeoffs later.
Turn “good” into measurable outcomes. Prefer a mix of product and operational signals.
Pick a small set (3–5). Too many metrics creates confusion; too few hides risk.
Describe the “happy path” in plain language, then list edge cases that will shape the architecture.
Happy path example: user signs in → searches a customer → sees current status → updates a field → audit log recorded.
Edge cases to surface early: offline/poor connectivity, partial permissions, duplicate records, high-volume imports, timeouts, retries, and what happens when a dependency is down.
Call out what you are not building in this version: integrations you won’t support yet, advanced analytics, multi-region, custom workflows, or full admin tooling. Clear boundaries protect schedules and make later “Phase 2” conversations easier.
Once these four pieces are written, the prompt becomes a shared contract. AI can help refine it, but it shouldn’t invent it.
A vague prompt often mixes goals (“make it easy”), features (“send notifications”), and preferences (“use serverless”) into one sentence. This step separates them into a requirements list you can design against.
Start by pulling out concrete behaviors and the moving parts they touch:
A good check: can you point to a screen, API endpoint, or background job for each requirement?
These shape architecture more than most people expect. Translate vague words into measurable targets:
Capture boundaries early so you don’t design an ideal system nobody can ship:
Write a few “done means…” statements anyone can verify, for example:
These requirements and constraints become the input to the candidate architectures you’ll compare next.
A vague prompt rarely fails because the tech is hard—it fails because everyone silently fills in missing details differently. Before proposing any architecture, use AI to pull those silent assumptions into the open and separate what’s true from what’s guessed.
Start by writing down the “defaults” people usually imply:
These assumptions strongly shape choices like caching, queues, storage, monitoring, and cost.
Ask the AI to create a simple table (or three short lists):
This prevents the AI (and the team) from treating guesses as facts.
Useful questions include:
Write assumptions down explicitly (“Assume peak 2,000 requests/min,” “Assume PII present”). Treat them as draft inputs to revisit—ideally linking each to who confirmed it and when. That makes later tradeoffs and architecture changes easier to explain, defend, and reverse.
A vague prompt rarely implies a single “correct” design. The fastest way to get to a production-ready plan is to sketch a few viable options, then choose a default and clearly explain what would make you switch.
For most early-stage products, start with one deployable backend (API + business logic), a single database, and a small set of managed services (auth, email, object storage). This keeps deployment, debugging, and changes straightforward.
Choose this when: the team is small, requirements are still shifting, and traffic is uncertain.
Same single deployable, but with explicit internal modules (billing, users, reporting) and a background worker for slow tasks (imports, notifications, AI calls). Add a queue and retry policies.
Choose this when: you have long-running tasks, periodic spikes, or need clearer ownership boundaries—without splitting into separate services.
Split a few components into separate services when there’s a strong driver: strict isolation (compliance), independently scaling a hotspot (e.g., media processing), or separate release cycles.
Choose this when: you can point to specific load patterns, org boundaries, or risk constraints that justify the added operational overhead.
Across these options, call out the differences explicitly:
A good AI-assisted output here is a small decision table: “Default = A, switch to B if we have background jobs, switch to C if X metric/constraint is true.” This prevents premature microservices and keeps the architecture tied to real requirements.
A surprising amount of “architecture” is really just agreeing on what the system’s data is, where it lives, and who is allowed to change it. If you model this early, later steps (components, interfaces, scaling, security) become far less guessy.
Start by naming the handful of objects your system revolves around—usually nouns from the prompt: User, Organization, Subscription, Order, Ticket, Document, Event, etc. For each object, capture ownership:
This is where AI is useful: it can propose an initial domain model from the prompt, then you confirm what’s real vs. implied.
Decide whether each object is primarily transactional (OLTP)—lots of small reads/writes that must be consistent—or analytical (aggregations, trends, reporting). Mixing these needs in one database often creates tension.
A common pattern: an OLTP database for the app, plus a separate analytics store fed by events or exports. The key is to align storage with how the data is used, not how it “feels” conceptually.
Sketch the path data takes through the system:
Call out risks explicitly: PII handling, duplicated records, conflicting sources (two systems claiming to be the truth), and unclear deletion semantics. These risks define boundaries: what must stay internal, what can be shared, and what needs audit trails or access controls.
Once you have boundaries and data in place, convert them into a concrete component map: what exists, what it owns, and how it talks to everything else. This is where AI is most useful as a “diagram generator in words”—it can propose clean separations and spot missing interfaces.
Aim for a small set of components with clear ownership. A good check is: “If this breaks, who fixes it, and what changes?” For example:
Pick a default communication style and justify exceptions:
AI can help by mapping each use case to the simplest interface that meets latency and reliability needs.
List third-party services and decide what happens when they fail:
Write a compact “integration table”:
This map becomes the backbone for implementation tickets and review discussions.
A design can look perfect on a whiteboard and still fail on day one in production. Before you write code, make the “production contract” explicit: what happens under load, during failures, and under attack—and how you’ll know it’s happening.
Start by defining how the system behaves when dependencies are slow or down. Add timeouts, retries with jitter, and clear circuit-breaker rules. Make operations idempotent (safe to retry) by using request IDs or idempotency keys.
If you call third-party APIs, assume rate limits and build backpressure: queues, bounded concurrency, and graceful degradation (e.g., “try later” responses rather than pile-ups).
Specify authentication (how users prove identity) and authorization (what they can access). Write down the top threat scenarios relevant to your system: stolen tokens, abuse of public endpoints, injection via inputs, or privilege escalation.
Also define how you’ll handle secrets: where they live, who can read them, rotation cadence, and audit trails.
Set capacity and latency targets (even rough ones). Then choose tactics: caching (what, where, and TTL), batching for chatty calls, async work via queues for long tasks, and limits to protect shared resources.
Decide on structured logs, key metrics (latency, error rate, queue depth), distributed tracing boundaries, and basic alerts. Tie each alert to an action: who responds, what to check, and what “safe mode” looks like.
Treat these choices as first-class architecture elements—they shape the system as much as endpoints and databases.
Architecture isn’t a single “best” answer—it’s a set of choices under constraints. AI is useful here because it can list options quickly, but you still need a clear record of why you chose one path, what you gave up, and what would trigger a change later.
| Option | Cost | Speed to ship | Simplicity | Scale headroom | Notes / When to revisit |
|---|---|---|---|---|---|
| Managed services (DB, queues, auth) | Medium–High | High | High | High | Revisit if vendor limits/features block needs |
| Self-hosted core components | Low–Medium | Low–Medium | Low | Medium–High | Revisit if ops burden exceeds team capacity |
| Monolith first | Low | High | High | Medium | Split when deploy frequency or team size demands |
| Microservices early | Medium–High | Low | Low | High | Only if independent scaling/ownership is required now |
Write down “acceptable failures” (e.g., occasional delayed emails) versus “must not fail” areas (e.g., payments, data loss). Put safeguards where failures are expensive: backups, idempotency, rate limits, and clear rollback paths.
Some designs increase on-call load and debugging difficulty (more moving parts, more retries, more distributed logs). Prefer choices that match your support reality: fewer services, clearer observability, and predictable failure modes.
Make the decision criteria explicit: compliance needs, customization, latency, and staffing. If you choose self-hosted for cost, note the hidden price: patching, upgrades, capacity planning, and incident response.
Great architectures don’t just “happen”—they’re the result of many small choices. If those choices live only in chat logs or someone’s memory, teams repeat debates, ship inconsistently, and struggle when requirements shift.
Create an Architecture Decision Record (ADR) for each key choice (database, messaging pattern, auth model, deployment approach). Keep it short and consistent:
AI is especially useful here: it can summarize options, extract tradeoffs from discussions, and draft ADRs that you then edit for accuracy.
Assumptions change: traffic grows faster, compliance becomes stricter, or an external API becomes unreliable. For each major assumption, add an exit ramp:
This turns future change into a planned move, not a fire drill.
Attach testable milestones to risky choices: spikes, benchmarks, small prototypes, or load tests. Record expected outcomes and success criteria.
Finally, version ADRs as requirements evolve. Don’t overwrite history—append updates so you can trace what changed, when, and why. If you need a lightweight structure, link to an internal template like /blog/adr-template.
A draft architecture isn’t “done” when it looks clean on a diagram. It’s done when the people who will build, secure, run, and pay for it agree it works—and when you have evidence to back up the tricky parts.
Use a short checklist to force important questions to the surface early:
Keep the output concrete: “What will we do?” and “Who owns it?” rather than general intentions.
Instead of a single throughput estimate, produce load and cost ranges that reflect uncertainty:
Ask the AI to show its math and assumptions, then sanity-check against current analytics or comparable systems.
List critical dependencies (LLM provider, vector DB, queue, auth service). For each, capture:
Make reviews explicit, not implied:
When disagreements remain, record them as decisions-to-make with owners and dates—then move forward with clarity.
AI can be a strong design partner if you treat it like a junior architect: capable of generating options quickly, but needing clear context, checks, and direction.
Start by giving the AI a “box” to work inside: business goal, users, scale, budget, deadlines, and any non-negotiables (tech stack, compliance, hosting, latency, data residency). Then ask it to list assumptions and unknowns first before proposing solutions.
A simple rule: if a constraint matters, state it explicitly—don’t expect the model to infer it.
If your goal is to go from “architecture plan” to “working system” without losing decisions in handoffs, a workflow tool matters. Platforms like Koder.ai can be useful here because the same chat that helps you clarify requirements can also carry those constraints into implementation: planning mode, repeatable iterations, and the ability to export source code when you’re ready to own the pipeline.
This doesn’t remove the need for architecture reviews—if anything, it raises the bar for documenting assumptions and non-functional requirements—because you can move from proposal to running app quickly.
Use short templates that produce structured output:
You are helping design a system.
Context: <1–3 paragraphs>
Constraints: <bullets>
Non-functional requirements: <latency, availability, security, cost>
Deliverables:
1) Assumptions + open questions
2) 2–3 candidate architectures with pros/cons
3) Key tradeoffs (what we gain/lose)
4) Draft ADRs (decision, alternatives, rationale, risks)
Ask for a first pass, then immediately request a critique:
This keeps the model from locking onto a single path too early.
AI can sound confident while being wrong. Common issues include:
If you want, you can capture the outputs as lightweight ADRs and keep them alongside the repo (see /blog/architecture-decision-records).
A vague prompt: “Build a system that alerts customers when a delivery will be late.”
AI helps translate that into concrete needs:
Two early questions often flip the design:
By writing these down, you prevent building the wrong thing quickly.
AI proposes candidate architectures:
Option 1: Synchronous API: carrier webhook → delay scoring service → notification service
Option 2: Queue-based: webhook → enqueue event → workers score delays → notifications
Tradeoff decision: choose queue-based if carrier reliability and traffic spikes are risks; choose synchronous if volume is low and carrier SLAs are strong.
Deliverables to make it buildable:
“Prompt to architecture” is the workflow of turning an intent (“build a customer portal”) into a buildable plan: requirements, assumptions, candidate options, explicit decisions, and an end-to-end view of components and data flows.
Treat AI output as a proposal you can test and edit—not as a final answer.
Production-ready means the design explicitly covers:
Diagrams help, but they’re not the definition.
Write 1–2 sentences that specify:
If the prompt doesn’t name a real user or urgency, ask for them—otherwise you can’t rank tradeoffs later.
Choose 3–5 measurable metrics mixing product and operational outcomes, for example:
Avoid “metric sprawl”: too many makes priorities unclear; too few hides risk.
List hidden defaults early (traffic, data quality, user tolerance for delays, on-call coverage), then split into:
Document assumptions explicitly (with who/when confirmed) so they can be challenged and revised.
Start with multiple viable options and pick a default with clear “switch conditions,” for example:
The point is traceable tradeoffs, not a single “correct” design.
Name core domain objects (nouns like User, Order, Ticket, Event) and for each define:
For each dependency (payments, messaging, LLMs, internal APIs), define failure behavior:
Assume rate limits exist and design backpressure so spikes don’t cascade into outages.
Use Architecture Decision Records (ADRs) to capture:
Add “exit ramps” tied to triggers (e.g., “if we exceed X RPS, add read replicas”). Keep ADRs searchable and versioned; a lightweight template can live at a relative link like /blog/adr-template.
Give AI a tight box: goal, users, scale, constraints (budget, deadlines, compliance, stack), and ask it to:
Then run “critique and refine” loops (what’s brittle, what’s missing, what to simplify). Watch for confident specifics it can’t verify and require explicit uncertainty where needed.
Then align storage with access patterns (OLTP vs analytics) and sketch the end-to-end data flow (ingestion → validation/enrichment → retention/deletion).