# What AI Safety Means for Applications — AI Safety, Evaluation and Cost Control

Source: https://www.geekswithgeeks.com/en/ai-safety/found-what-safety

> Define application-level safety as preventing harm to users, the business and third parties.

## Safety is a product property

When you build with a language model you are responsible for how the **whole application** behaves, not only the model. **AI safety** here means reducing the chance and impact of harm: wrong or invented answers, harmful content, leaks of private data, unfair treatment, misuse by bad actors, and over-trust by users. Model providers add their own safeguards, but they cannot know your users, data or context, so application-level controls are still your job.

## Layers of protection

Safe LLM applications combine good prompts, filters, grounded data, limited tools and human oversight.

![Four layers: input, model, output, oversight.](assets/figures/ai-safety/section-1-map.svg) — Figure 1.1 — Input, model, output and oversight.

## A restaurant kitchen

A supplier delivers safe ingredients, yet the restaurant still needs hygiene rules, allergy labels and temperature checks. The supplier's quality does not replace the kitchen's responsibility.

## Start with who could be harmed

For each feature, list who could be hurt and how (a student given wrong medical advice, a customer whose data is exposed, a person described unfairly). The list tells you where safeguards matter most.

**Quiz:** Who is responsible for how an LLM application behaves?

- [ ] Only the model provider
- [ ] Nobody
- [x] The team that builds and deploys the application
- [ ] Only the end user

*Answer:* The team that builds and deploys the application. Providers add safeguards, but the application owner controls context, data, tools and deployment.