Checklist · AI
AI Feature Launch Checklist
Opens with creditsA go-live checklist for putting an AI feature in front of real users. It covers whether the use case fits, the evals that must pass, UX for uncertainty and errors, disclosure and human override, abuse and safety filters, rate limits and cost caps, privacy of prompts, and monitoring, staged rollout and rollback, each with a severity and a concrete way to verify it.
Written by Tauseef Fayyaz8 chapters, 34 checksAbout 16 minReviewed 13 October 2026What you will learn
8 chapters · about 16 min
1Use case fitBefore anything else, confirm the feature is a good job for a language model, and that the cost of its mistakes is one your users and business can carry.4 checks · 2 min
- 1Write down the user problem and the non-AI baseline the feature must beat
- 2Assess what happens when the AI is wrong, and design the feature around those stakes
- 3Define the success and guardrail metrics you will watch after launch
- 4Name one owner who can pause or roll back the feature
4 in this chapterOpen this chapter2Evals passedA launch decision needs numbers, not impressions. These checks confirm the feature was measured against an agreed bar, including how it fails.4 checks · 2 min
- UX for uncertainty and errorsUsers need to know when to trust the output and what to do when it is wrong. These checks follow Google PAIR's guidance on errors and graceful failure.5 checks · 2 minOpens with credits
- Disclosure and human overridePeople should know when they are dealing with AI and be able to reach a person when it matters. These checks cover labels, escalation and opting out.4 checks · 2 minOpens with credits
- Abuse and safety filtersOnce public, the feature will be used in ways you did not intend. These checks put filters, identity and reporting in place before that happens.4 checks · 2 minOpens with credits
- Rate limits and cost capsA launch can multiply traffic overnight, and abuse can multiply cost. These checks make sure neither surprises you.3 checks · 1 minOpens with credits
- Privacy of prompts and outputsPrompts often contain personal or confidential data. These checks cover what you send, what your provider keeps and what you log.4 checks · 2 minOpens with credits
- Monitoring, rollout and rollbackLaunch gradually, watch the right signals, and be able to undo it fast. These checks apply standard release safety to a component that can change without a deploy.6 checks · 3 minOpens with credits
About this checklist
An AI feature can pass every demo and still fail on launch day. Real users ask things nobody tested, some of them try to break it on purpose, a provider has an outage, and the first bill arrives larger than the forecast. This AI feature launch checklist collects the checks a team should be able to tick before turning an LLM feature on for real users, and the ones that keep it healthy in its first weeks.
It is written for the launch decision, not for building the feature. Detailed guidance on evals, retrieval, agents and cost lives in their own checklists; here each topic is reduced to what must be true on the day you ship: the evals have passed against an agreed rule, users can tell when the AI is unsure or wrong and have a way forward, someone can turn it off in minutes, and you know what it costs per user.
The checks draw on Google's People + AI Research guidance on errors and graceful failure, OpenAI's safety best practices, the OWASP Top 10 for LLM Applications, NIST's Generative AI Profile (NIST AI 600-1) and Google's SRE guidance on canary releases. Where a check touches law, such as telling people they are talking to an AI, it notes the rule but is not legal advice.
Each check has a severity (must, should or nice) and a concrete way to verify it, such as a screen to look at, a test to run or a number to read off a dashboard. A useful habit is to copy the must checks into your launch ticket and attach the evidence for each one.
Common questions
What should I check before launching an AI feature?
At minimum: the feature passed an agreed ship rule on a realistic eval set including attack prompts, users can see its limits and have a path forward when it fails, AI output is disclosed, risky actions need confirmation, per-user limits and spend caps are set, prompt data handling matches your privacy notice, and a named owner can turn it off with a flag.
How do I roll out an AI feature safely?
Put it behind a runtime flag, release it to internal users, then to a small share of real users, and compare quality, safety, latency and cost against defined thresholds before each step. Pin the model and prompt version so behaviour cannot change without a deploy, and plan what happens when the provider is down.
Do I need to tell users a feature uses AI?
In many cases, yes, and it is good practice everywhere. In the EU, Article 50 of the AI Act requires people to be informed when they interact with an AI system unless it is obvious, and certain generated content must be marked. Check the rules for your markets with counsel.
How do I handle AI errors in the user interface?
Set expectations at the point of use, show sources where answers need checking, let users edit or undo AI actions, and give every failure a path forward such as retrying, rephrasing, a manual route or a person. Google PAIR's guidance on errors and graceful failure covers these patterns in detail.
Sources (8)
Checked against these, last on 13 October 2026.
- Errors + Graceful Failure, Google People + AI Researchpair.withgoogle.com/chapter/errors-failing/
- Safety best practices, OpenAI API docsdevelopers.openai.com/api/docs/guides/safety-best-practices
- Data controls in the OpenAI platform, OpenAI API docsdevelopers.openai.com/api/docs/guides/your-data
- OWASP Top 10 for LLM Applications 2026, OWASP GenAI Security Projectgenai.owasp.org/resource/owasp-genai-llm-top-10-2026/
- LLM10:2025 Unbounded Consumption, OWASP GenAI Security Projectgenai.owasp.org/llmrisk/llm102025-unbounded-consumption/
- Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile (NIST AI 600-1), NISTwww.nist.gov/publications/artificial-intelligence-risk-management-framework-generative-artificial-intelligence
- AI RMF Core, NIST AI Resource Centerairc.nist.gov/airmf-resources/airmf/5-sec-core/
- Canarying releases, Google SRE Workbooksre.google/workbook/canarying-releases/
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See all- Best Practices for Building LLM AppsHow to build LLM apps and agents that hold up in production: prompts, evals, prompt injection, tool permissions, cost, logging and privacy.
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See all- RAG Production ChecklistA RAG production checklist: chunking, embeddings, hybrid search, reranking, per-user permissions, citations, evals, re-indexing and cost, with how to verify each check.
- LLM Evaluation ChecklistAn LLM evaluation checklist: success criteria, eval datasets, code and model graders, judge calibration, agent evals, safety tests and production monitoring, with how to verify each check.
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See all- GenAI RAG app (FastAPI) project structureFor developers turning a "chat with your documents" demo into an app people can rely on. A FastAPI backend where every layer has a home: ingestion workers, retrieval, prompts as versioned files, model clients with fallbacks and cost caps, guards, evals that gate CI, and tracing, so a wrong answer can be traced to the step that caused it.
- AI agent with an MCP server project structureFor developers who want to give AI assistants safe access to their own data or API. A TypeScript MCP server built on the official SDK, organised by feature (tasks, projects) with each feature owning its tools, resources and prompts, served over stdio for local hosts and Streamable HTTP with OAuth for remote ones, plus a small agent and the evals that prove a model can use it.
- Machine learning project project structureFor students and analysts whose model works in a notebook and now needs to be reproducible, reviewable, served and watched. It starts from the Cookiecutter Data Science v2 layout (immutable raw data, numbered notebooks, a real Python package) and grows into versioned data with DVC, typed parameters, data validation with pandera, experiment tracking and a model registry with MLflow, a FastAPI prediction service, drift monitoring and, for a team, scheduled retraining, model cards, fairness checks and shadow deployment.
Career questions
See all- Which AI tools do employers expect junior developers to know in 2026?Employers rarely require a specific brand. They expect you to use an AI coding assistant well (in the editor and in the terminal), to review and test what it produces, to call an LLM API in a small feature, and to explain when you would not use AI. Pick one assistant, learn it properly, and show judgement in your projects.
- How do I use AI to become a better engineer instead of becoming dependent on it?Decide the job AI does before you open it. Use it as a tutor, reviewer and sparring partner that explains and challenges, not a vending machine that hands you finished code. Attempt first, ask second, and always be able to explain and defend what you ship.
- How do I start learning AI agents as a software engineer?Start with one LLM API and plain code: a loop that calls the model, lets it pick a tool, runs the tool and feeds the result back. Build one small agent that does a real task end to end, add evaluation and guardrails, and only then reach for a framework. Agents are software engineering first.
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