Cross the gap between 'tests pass' and 'safe in production' — assess release readiness, plan the deploy and its rollback, and set up the monitoring and launch checks before you ship, not after.
'The tests pass' and 'it's safe in production' are not the same statement, and the gap between them is where launches go wrong. Tested code still has to ship: someone has to judge whether it's actually ready, plan how it rolls out and how it rolls back, decide what to watch, and confirm the launch is healthy instead of hoping. Skip that and a green build becomes a 2am incident. This workflow owns the tested-to-shipped gap — release readiness, deployment and rollback planning, monitoring, and launch validation — so shipping is a deliberate step, not a leap.
Each step uses an existing NewPrompt tool, pre-filled by a matching resource. Open the resource to read it, or jump straight into the tool with the inputs ready.
Put the model in a release engineer's seat
Anchor the model to think like someone shipping to production — reasoning about deploys, rollbacks, and what breaks at launch — instead of someone writing the next feature. The mindset is risk and recovery, not code.
Run a release-readiness review
Name the gaps between 'tests pass' and 'safe to ship': config and secrets, migrations, feature flags, dependencies, and the checks a green build doesn't cover. Readiness is a decision, and this is where you make it honestly.
Plan the deploy and the rollback
Lay out how the change reaches production — the sequence, the migration order, the cutover — and, for each step, how you undo it. A deploy plan without a rollback plan is a bet, not a release.
Set up monitoring and launch validation
Decide what to watch and how you'll know the launch is healthy — the signals, the thresholds, the checks that confirm success and the ones that trigger a rollback — and write it into a runbook before you ship.
A release you can ship with your eyes open — readiness assessed honestly, a deploy plan with a rollback for every step, and monitoring plus launch validation written into a runbook — so tested code crosses into production deliberately instead of becoming the next incident.
Use this when you're shipping a tested change and nothing has broken yet — it plans readiness, rollout, rollback, and launch checks before go-live. Use the AI Production Incident Workflow once production is already down and you're diagnosing a live outage. This one prevents the page; that one answers it.
No — and that's the whole point of this workflow. Tests prove the code does what you asked; they say nothing about config, migrations, rollout order, rollback, or whether you'll notice a bad launch. This owns exactly that gap between tested and shipped.
No. It produces the readiness call, the deploy-and-rollback plan, and the launch runbook — the thinking that makes a release safe. Running the actual deploy, and owning the go/no-go, stays with you.
Four artifacts you take into your release: an honest readiness assessment naming what blocks the ship, a deploy sequence with a rollback path for every step, and a launch runbook of signals, thresholds, and health checks. NewPrompt supplies the prompts and step order; you run them in your AI tool.
Work the four steps in order in your own AI tool: cast the model as a release engineer, run a release-readiness review, plan the deploy and its rollback, then define monitoring and launch validation. Each step links a prompt tool and resource; you paste your change details and own every judgment.
Bring your tested change plus its release context: config and secrets, pending migrations, feature flags, dependency changes, and your rollout target. Step 2 turns those into a readiness call — the sharper your inputs on migration order and cutover, the more honest the deploy and rollback plan in step 3.
Complete build journeys that include this workflow as a stage.
The full path from idea to a shipped SaaS MVP — define and scope the requirements, design the architecture, API, and data model, then build it reviewed, tested, secured, cost-controlled, and deployed.
The full path to an AI document processing system — define the use case, design the intake pipeline, extract fields from unstructured documents, classify and route them, pin the output contract, evaluate accuracy, then ship it monitored.
The full path to an AI content moderation system — define the policy and label taxonomy, extract signals from user content, classify it against policy, emit structured decisions, evaluate false positives and negatives, wire enforcement and review queues, review abuse risks, then ship.
The full path to a backend you can put clients on — define the requirements, design the architecture, API contract, data model, and access control, then build it reviewed, tested, secured, and shipped.
The full path to a business website that holds together — plan the content, structure the site, design the components, write the page copy, then ship it as one coherent whole.
The full path to taming an inherited codebase — understand it, document its architecture, pin its behavior with tests, then refactor, modernize, review, speed up, and ship it without breaking what works.
The full path to docs people can actually navigate — plan the doc set, structure the site, write the guides and the API reference, then ship it as a coherent documentation site.
The full path to automation that survives the real world — wire the integrations and triggers, design the control API, move the data through validated stages, evaluate the AI steps, then deploy.
The full path to a support operation, not just a bot — stand up the knowledge base, route the tickets, add the AI agent, integrate your stack, close the feedback loop, evaluate, and deploy.
The full path to an applicant tracking system — model jobs, candidates, and hiring stages, generate job descriptions and screening prompts, parse résumés into structured data, design the hiring API, set roles, review security, then ship.
The full path to a booking and reservation system — model resources, availability, and reservations, design the booking API, set customer accounts, wire calendar and notification integrations, design the UI, review security, then ship.
The full path to an inventory management system — model products, stock, locations, and movements, design the inventory API, set roles, plan supplier and POS integrations, design the UI, review data-integrity risks, then ship.
The full path to an internal admin panel — model the operational data it manages, design the management API, define RBAC and admin permissions, design the tables and forms, review risky admin actions, then ship to trusted internal users.
The full path to knowledge that's findable by people and AI — plan the taxonomy, structure it for search, write the articles, tag the metadata, make it retrievable, then ship it maintainable.
The full path to a two-sided platform — define the buyer-and-seller requirements, model the data, design the API, build roles and permissions, wire integrations, design the UI, then test, secure, and ship it.
The full path to a CRM that fits your sales process — define the contacts, deals, and pipeline, model the data that ties them together, then build the roles, integrations, and pipeline UI, and ship.
The full path to a store you own end to end — model the catalog and orders, design the storefront and checkout, add customer accounts and payments, then secure it, test it, and ship.
The full path to a pipeline that moves data without corrupting it — design the ingestion and transforms, extract and structure the sources, gate the quality, store it, then deliver and ship it monitored.
"Bump the tag and restart — ask Priya if it breaks" is not a runbook, and Priya is on a plane. Here's how to turn what you actually do into a deployment runbook someone else can execute: preconditions, ordered steps, a verification at every gate, a failure path that isn't hope.
Turn a release into a staged rollout plan you can defend — cohorts built from your risk rather than round numbers, gates that widen on evidence instead of a timer, and a written difference between pausing and rolling back.
Turn "are we good for Thursday?" into a checklist that can answer it — every item carrying the evidence behind it, a name, and an honest status, so the ship decision rests on what somebody checked rather than what nobody objected to.
Decide what deserves to wake somebody up. Plan monitoring from the journey that has to work rather than the components you happen to have — signals sorted into page, ticket and dashboard, and an alert for the failure that makes no noise at all.
Work a live production incident in the right order — triage and stabilize first, then find the cause, then write the summary and postmortem — so the fire is out before the writeup begins.
Build a test suite that fails for real reasons, not green decoration — coverage across unit, integration, and edge cases, then a review for the gaps.
Review code for what an attacker would do, not just what tests catch — anchor the model as a security engineer, run a threat-focused review, then back the findings with auth and input tests.