A prototype that impresses in a demo is a real achievement, and it is not yet production software. The gap between the two is mostly unglamorous work: requirements, tests, security, monitoring, and clear ownership. This checklist helps you see that gap before you promise a launch date.
1. Requirements and scope
- Write down what it must do and for whom. Include the users, the main flows, and what is out of scope.
- Define success. Decide how you will know it works: accuracy, speed, adoption, or cost, with numbers you can test.
- Name an owner. A person is accountable for the result after launch.
2. Architecture and integrations
- Document how it works. Components, data flows, external services, and dependencies.
- Plan the integrations. Authentication, permissions, rate limits, and failure behavior for every system it touches.
- Decide what happens when a service is down. Retries, fallbacks, and clear error messages.
3. Quality and testing
- Test with representative data, including hard cases. A handful of good examples is not a test suite.
- Build evaluations for AI behavior. OpenAI's guidance recommends building tests and evaluation suites that measure prompt behavior, and Anthropic's guidance assumes you can test empirically against defined success criteria.
- Pin model versions where consistency matters. OpenAI recommends pinning specific model snapshots for consistent behavior.
- Automate regression checks. Changes to prompts, models, or code should not silently break what worked.
4. Security and data
OWASP's Top 10 for LLM Applications (2025) is a useful checklist for what can go wrong. Review at least these risks:
- Prompt injection. Can inputs change the system's behavior in unintended ways?
- Sensitive information disclosure. Can it expose data it should not?
- Excessive agency. Does it have more access or autonomy than the task needs?
- Improper output handling. Is its output validated before another system or a person acts on it?
- Unbounded consumption. Can it consume excessive resources or cost?
Also confirm what data goes to which provider, who can access it, and how long it is kept.
5. Human oversight
- Decide where a person reviews. Anthropic's agents guide notes that agents can pause for human feedback at checkpoints or when they hit blockers and, for coding agents, that human review remains crucial for aligning solutions with system requirements.
- Define escalation. What happens when the system is unsure or something looks wrong?
- Document decisions so reviews are traceable.
6. Operations and monitoring
- Log what matters without logging sensitive data you should not keep.
- Monitor quality, errors, latency, and cost. Set thresholds and alerts.
- Prepare to roll back. You should be able to return to a known good version quickly.
- Plan maintenance. Models, prompts, and dependencies change, and someone needs to own updates.
7. Documentation and handover
- Write it down. How it works, how to run it, how to test it, and how to fix common problems.
- Make sure the knowledge stays with your team. Code, configuration, and decisions should not live in one person's head.
8. Launch plan
- Start small. A limited group, a limited scope, and a way to watch results closely.
- Define success and stop conditions for the first weeks.
- Collect feedback from the people who use it.
How to use this checklist
Go through each section and mark what is done, what is missing, and what is a risk. The result is a short, honest list of work between your prototype and a launch. If you want a team to take that work on, an AI Pod adds requirements, architecture, tests, security review, documentation, and release readiness around a prototype, with human review at the decisions that matter. To understand how the model differs from a traditional team, read what an AI Pod is.