Shipping an AI feature is different from shipping ordinary software. Traditional code does the same thing every time. AI output varies, can be confidently wrong, and can be steered by the very content it reads. None of that is a reason to avoid AI, but it is a reason to launch with guardrails.

Use this checklist before your AI feature reaches real users. Not every item applies to every product, but each one is worth a deliberate decision.

1. Wrong or invented answers

AI models sometimes produce answers that sound right but are not, often called hallucinations.

  • Ground answers in your own sources where possible, and show citations so users can check.
  • Allow "I don't know." Instruct the system to say so when it lacks information, and test that it actually does.
  • Match the stakes. For high-impact outputs such as prices, medical, legal, or financial information, keep a human review step.
  • Label AI output clearly so users know when to double-check.

2. Private and sensitive data

  • Know what data goes where. Map which information is sent to which model provider, and check their data retention and training policies.
  • Send only what is needed. Strip personal or confidential data that the task does not require.
  • Respect permissions. An AI feature must never show a user information they could not access otherwise.
  • Be careful with logs. Conversation logs are valuable for improvement but may contain sensitive data. Decide who can see them and how long they are kept.

3. Prompt injection and misuse

If your AI reads content from users, websites, emails, or documents, that content can contain instructions designed to hijack it. This is called prompt injection.

  • Treat external content as data, not instructions. Design prompts and system logic so that content the AI reads cannot change its rules.
  • Limit what the AI can do. If it can take actions such as sending emails, editing records, or making payments, give it only the permissions it truly needs.
  • Confirm important actions. Require a human click before anything irreversible happens.
  • Test with adversarial inputs. Try to break your own feature before someone else does.

4. Harmful or off-brand output

  • Define what is out of scope. Decide which topics the feature should decline and how it should respond.
  • Use moderation where appropriate, especially for public-facing features that accept open-ended input.
  • Set the tone. Make sure answers match your brand voice and do not make promises your business cannot keep.

5. Reliability and cost

  • Plan for outages. Decide what users see if the model provider is slow or unavailable.
  • Set limits. Rate limits and spending alerts protect you from runaway costs and abuse.
  • Watch latency. Slow responses feel broken. Stream output or show progress where it helps.

6. Monitoring after launch

Guardrails are not finished at launch.

  • Review real conversations regularly, especially those users rated poorly or abandoned.
  • Keep a test set of real and tricky questions, and run it whenever you change prompts, models, or data.
  • Make reporting easy. Give users a simple way to flag a bad answer.
  • Assign an owner who is responsible for quality and incidents.

7. Transparency and compliance

  • Tell users they are interacting with AI. It builds trust and is required in some places.
  • Check the rules that apply to you. Depending on your industry and region, privacy and AI regulations may set specific requirements. When in doubt, ask a legal advisor.
  • Document your decisions. A short record of risks considered and safeguards chosen helps with audits, customers' security reviews, and future changes.

A quick pre-launch summary

Before going live, you should be able to answer yes to these:

  • We know what data the feature uses and where it goes.
  • Users can tell when content is AI-generated and can verify important answers.
  • The AI cannot take irreversible actions without confirmation.
  • We have tested with difficult and adversarial inputs.
  • Someone owns monitoring and has a plan for incidents.

Guardrails do not make a product slower to build when they are part of the design from the start. They make it easier to launch with confidence. If you would like a review of an AI feature before launch, we are happy to help.