# AI-native building desk

Build and operate a product that depends on a model, with the risks stated.

Canonical URL: https://worldenterprise.org/insights/topic/ai/
Last updated: 2026-10-10
Publisher: World Enterprise, run by World Enterprise Group Incorporated

AI-native building means putting a language model inside a product and running it with the risks stated.

One article reads the OWASP list of risks for model applications. Another applies the NIST AI Risk Management Framework to a small team. Another sets out the questions to ask a model provider. Another shows how to evaluate a feature before launch. Another explains retrieval and where it breaks. Another covers metering and budgeting usage. Another covers decisions about customer data. Another places a person in the loop. The last lists questions to bring to counsel about AI regulation.

Each article cites the document it describes and says what it cannot tell you.

## Articles

- [AI features and customer data: what to decide first](https://worldenterprise.org/insights/ai-features-and-customer-data-what-to-decide-first/): The decisions to make before customer data reaches a language model: what goes in, which account tier, how long it is kept, and what customers are told.
- [AI regulation for a small SaaS company: what to ask counsel about the EU AI Act and the Texas AI statute](https://worldenterprise.org/insights/ai-regulation-for-a-small-saas-company-what-to-ask-counsel/): Questions a small SaaS company can bring to counsel about the EU AI Act and the Texas AI statute, with dated facts from each primary source.
- [Choosing a model provider: the questions that matter](https://worldenterprise.org/insights/choosing-a-model-provider-the-questions-that-matter/): Questions to put to any language model provider before you build on it, drawn from vendor documentation on data use, retirement notice, limits and terms.
- [Evaluating an LLM feature before you ship it](https://worldenterprise.org/insights/evaluating-an-llm-feature-before-you-ship-it/): A method for testing a language model feature before launch: written success criteria, a test set with edge cases, grading choices and re-runs after changes.
- [Human review in an AI feature: where to keep a person in the loop](https://worldenterprise.org/insights/human-review-in-an-ai-feature-where-to-keep-a-person-in-the-loop/): Where a person should review an AI feature's output, how review fails when it becomes a rubber stamp, and what standards and laws say about oversight.
- [Metering and budgeting model usage](https://worldenterprise.org/insights/metering-and-budgeting-model-usage/): How to measure language model usage per feature and per customer, set limits before a bill surprises you, and handle the errors that limits produce.
- [Prompt injection and the OWASP Top 10 for LLM applications](https://worldenterprise.org/insights/prompt-injection-and-the-owasp-top-10-for-llm-applications/): What prompt injection is, how the OWASP Top 10 for LLM applications describes it, and the design questions the list raises for a small SaaS team.
- [Retrieval-augmented generation: what it is and where it breaks](https://worldenterprise.org/insights/retrieval-augmented-generation-what-it-is-and-where-it-breaks/): What retrieval-augmented generation is, where it commonly fails in a product, and how to test retrieval and answers separately before launch.
- [The NIST AI Risk Management Framework for a small team](https://worldenterprise.org/insights/the-nist-ai-risk-management-framework-for-a-small-team/): What the NIST AI Risk Management Framework contains, how its four functions read at the scale of a small team, and what the framework does not say.