Shipping AI to Prod in 7 Days

10/15/2025 • RiseGravity Team

Shipping AI to Prod in 7 Days

From concept to production

Shipping AI to production fast is less about hype and more about disciplined scope, ruthless focus, and great guardrails. Here’s a proven, one‑week plan we use to launch reliable AI features that users love—without compromising safety, latency, or costs.

TL;DR

  • Start narrow, measure everything, ship behind flags, iterate with real data.
  • Treat AI like any other production system: SLOs, budgets, fallbacks, and evaluations.
  • Own the UX. The best AI ships when the interface makes success obvious.

What worked

  • Clear success metrics from day one (task success, p95 latency, cost/session)
  • Tight feedback loops with real users (pilot cohort + session replays)
  • Guardrails for safety, latency, and cost (SLOs, fallbacks, spend caps)

7‑Day Blueprint

  • Day 0–1: Problem & Scope — Define a single user story (who/what/when). Write success criteria and non‑goals. Select model family (e.g., GPT‑4o for reasoning; small instruct for low‑latency paths). Draft SLOs: p95 < 1.5s, accuracy > X on eval set, <$Y per 100 tasks.

  • Day 2: Data & Eval Harness — Collect 30–100 real examples + expected outputs (including edge cases). Build an automatic evaluation harness (exact/semantic, rule checks, red‑flag detectors).

  • Day 3: Prototype (Vertical Slice) — Implement minimal end‑to‑end: prompt, (optional) RAG, tool calls, output shaping. Add structured logs: prompt+vars hash, token usage, latency, model version, user outcome.

  • Day 4: Safety & Cost Controls — Input/output sanitization, PII filters, jailbreak prevention, profanity/brand checks. Set cost caps and concurrency limits; cache high‑value sub‑steps.

  • Day 5: UX & Fallbacks — Inline results with clear affordances. Show confidence and provide one‑click “Improve” loop. Add fast fallback (smaller model / pre‑computed answer) when SLOs are exceeded.

  • Day 6: Dark Launch — Ship behind a feature flag. Enable for internal + pilot users only. Compare eval + real‑traffic metrics. Fix the top 3 issues.

  • Day 7: Rollout & Announce — Expand cohort. Add documentation and in‑product tips. Monitor SLO dashboards; iterate prompts and retrieval.

Architecture & Tooling

  • Model provider: OpenAI (GPT‑4o/GPT‑4o‑mini) for reasoning; small instruct for quick passes
  • Retrieval (RAG): semantic search over curated sources; citations linked in UI
  • Orchestration: n8n or a lightweight in‑app pipeline with retries + backoff
  • Observability: token/latency budgets, cost per session, outcome labeling, eval regression tests
Client → API (guardrails) → Retrieval (vector/db) → LLM (tools/prompts) → Output shaping → UI
                                  ↘ logs/metrics/evals ↙

Evaluations that matter

  • Functional: exact/semantic match to references; rule-based correctness checks
  • Safety: PII leaks, brand/jailbreak filters, blocked content ratio
  • UX: time‑to‑first‑value, edit rate, follow‑up rate
  • Cost/Perf: tokens per task, p95, cache hit rate, retries

Production guardrails you should copy

  • Timeouts + circuit breakers on third‑party calls
  • Idempotency keys for user‑initiated actions
  • Prompt templates as versioned assets (A/B safely)
  • Feature flags, per‑tenant limits, and per‑route spend caps

Common pitfalls (and fixes)

  • “Works on 10 examples, fails in prod” → Add a labeled eval set; run on every change
  • “Latency spikes during demo” → Pre‑warm, add caches and progressive disclosure UI
  • “Costs creep up” → Track spend/user; cap retries; use smaller models on easy paths
  • “Answers drift” → Freeze prompt versions; run nightly regression evals

If you want us to help ship an AI feature safely in a week, reach out at contact@risegravity.com or see recent work on our Projects.