If you’re tired of shipping demos that crumble under real users, it’s time to move from “it works on my laptop” to disciplined, observable reliability. This article is a field guide to building a Minimum Reliable Product (MRP) for AI-enabled apps—lean enough to move fast, but sturdy enough to trust. To make this concrete, I’m sharing an opinionated checklist, and the printable version lives at this page if you want to keep it by your keyboard.
Why MVP thinking isn’t enough for AI features
MVPs prioritize learning with minimum effort. That’s useful—until your feature meets real-world variance: inconsistent inputs, sudden traffic spikes, model drift, flaky third-party APIs, data schema surprises, or privacy constraints. AI amplifies this fragility. A successful MVP proves “someone wants it”; an MRP proves “someone can reliably get it, repeatedly, under stress.”
MRP thinking shifts your center of gravity from “Can we ship?” to “Can we keep it healthy when prod gets weird?” It keeps your stack nimble, but insists on three non-negotiables: observability, safe degradation, and closed-loop improvement.
The core idea, in one sentence
An MRP is the smallest slice of product that delivers a valuable outcome with guardrails: you can see when it fails, it fails small instead of catastrophically, and you have a built-in way to make it better next week than it was this week.
The 7-step MRP sprint (you can run this in two weeks)
- Define one critical user journey and cut it to the bone. Pick a single “happy path” that actually matters (e.g., “upload a PDF → ask a question → get an answer in <3s”). Everything else waits.
- Make failure visible before you code business logic. Instrument basic health using the four golden signals—latency, traffic, errors, saturation—so you can tell the difference between “slow,” “broken,” and “overloaded.” A quick primer on these is in Google’s SRE materials: golden signals.
- Engineer for blast radius, not perfection. Add a circuit breaker for upstream calls, timeouts with sensible budgets, and a fallback mode (e.g., rules-based response or cached answer) that still delivers something safe and helpful when the model or a dependency misbehaves.
- Treat the model like code that must be verified. Record the exact model version, prompt/template, and feature flags in a model registry (even a tagged object store + JSON manifest works). Require a small pre-prod smoke test before a new model/prompt goes live.
- Close the loop with user feedback in-product. Add a one-tap report (“Helped / Didn’t help”) and a short text field. Route negative signals to a triage inbox. You don’t need consensus to act—three clear reports on the same issue usually justify a patch.
- Automate the unglamorous. Ship a daily drift check on key input stats and answer quality proxies; if thresholds trip, alert a human (not a Slack firehose—one channel, one template).
- Write the two-page runbook you’ll need at 3 a.m. Page one: how to tell what’s wrong, fast. Page two: how to safely roll back model/prompt/config, and how to turn on degraded mode.
A minimal architecture that survives real users
Keep it boring by design. Start with a thin API that does authentication, rate limiting, and request validation (reject garbage early). Add a feature builder that standardizes inputs (tokenization, truncation, PII scrubbing), and log the canonical request/response pair with redaction. Call your inference layer with timeouts and a budget (for LLMs, don’t be afraid of a strict max tokens output to keep latency predictable). Cache frequently requested results for short TTLs; a 30–120s cache often cuts p95 latency and cost without harming freshness.
Your fallback path should be explicit: if inference exceeds budget or returns an invalid shape, switch to a deterministic response (template + rules + last known good answer). Users prefer a slightly more generic answer now over a perfect answer never.
What to measure (and what to ignore)
MRP metrics start at the edges: time-to-first-token (or first byte), request % under a user-visible latency budget, user-rated helpfulness, and degradation rate (how often you fall back). Internally, track saturation and error budgets. Resist vanity metrics; if a number won’t change your next decision, don’t put it on the dashboard.
For AI quality, pick two cheap proxies: a lightweight classifier that flags nonsensical or off-policy answers, and a rule that checks structural correctness (e.g., JSON schema validation). Combine these with user feedback to decide whether to roll forward, roll back, or fine-tune prompts.
Security and privacy without slowing to a crawl
Reliability and trust are siblings. Adopt a small, repeatable security checklist: input validation, output filtering, secrets management, and least-privilege service accounts. For a practical baseline, map your software practices to NIST’s Secure Software Development Framework; you don’t need enterprise ceremony to benefit from its guardrails—see the official summary here: NIST SSDF (SP 800-218).
Data handling should be explicit: tag fields that may contain PII, scrub them from logs by default, and create a “break-glass” path for limited, audited access when investigating incidents. Default to privacy-preserving analytics: aggregate, sample, and anonymize.
Incident playbook you’ll actually use
When pages fire, you need clarity, not cleverness. First, identify what changed: model, prompt, dependency, traffic mix, or data shape. If you can’t establish a cause in five minutes, roll back to the last known good config. Announce degraded mode to affected users with plain language and a clear expectation: “Responses may be shorter or less specific for the next 30 minutes while we stabilize.” Capture the timeline as you go—timestamps, actions taken, observed effects—so your post-incident review writes itself.
After the dust settles, pick one systemic fix. Maybe it’s a more aggressive timeout, a stricter schema validator, or a narrower prompt with guardrails. Ship that within 48 hours. MRP culture is about fast learning cycles, not perfect first drafts.
How to iterate without breaking the ship
Create a small change queue and batch deploys daily. Each change carries a “blast radius” estimate and a rollback plan. For AI changes, run a champion/challenger split at 5–10% traffic first. If it holds, promote to 100%; if it wobbles, demote without shame. Celebrate quiet deploys—no fanfare, no drama, just a steady march of better.
Common failure patterns (and the counter-moves)
Over-promising latency with under-budgeted prompts. Fix: cap max tokens and measure p95 religiously.
Silent partial failures (half your upstream calls time out). Fix: structured logs and a single SLO view that makes “partial outage” a visible state.
One-off heroics instead of system fixes. Fix: “one bug, one systemic prevention” as a rule.
Unbounded retries that melt your own service. Fix: exponential backoff with jitter and a hard ceiling.
Culture matters more than tools
Tools are multipliers for habits you already have. The habit that wins: make small promises and keep them relentlessly. The first time a user notices your feature quietly “just works,” you’ve crossed the line from prototype to product. Keep shipping that feeling.
Your two-week MRP starter plan
Day 1–2: instrument the golden signals; define one user journey and a 3s budget.
Day 3–4: implement timeouts, circuit breaker, and explicit fallback.
Day 5: add model/prompt versioning and a smoke test.
Day 6: wire one-tap feedback and start a triage doc.
Week 2: cache hot paths, add drift checks, write the two-page runbook, and do a 10% champion/challenger release.
You won’t get medals for this work. You’ll get something better: users who stay, devs who sleep, and a platform you can trust to carry bigger ideas.
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