AI Academy · Module 5: Safety and Application

Designing Useful Real-World AI Applications

Identify a problem suitable for AI, choose appropriate tools, measure outcomes and keep people accountable.

Lesson 11 of 11 · Reference topic 19 · Advanced learner pilot · Educational review pending

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Start with the big idea

The best learning project is not the flashiest demo. It is a narrow problem with clearly defined inputs, success criteria, human responsibility and a realistic way to test failures.

Understand it in more detail

Start with a user and a repetitive or difficult task. Determine whether simple rules, search, traditional statistics or generative AI are appropriate. Do not introduce an LLM when deterministic code solves the problem more safely.

Map inputs, data permissions, model or algorithm, optional retrieval and tools, user-facing output, approval, monitoring and failure recovery. Specify who makes consequential decisions and what happens when the system is uncertain.

Use a small, representative test set with both normal and adversarial examples. Evaluate correctness, task time, error severity, privacy exposure and operating cost against a non-AI baseline. Stop or revise if the system does not improve the task.

Worked example

A fictional school library receives book-location questions. A basic indexed search may answer most queries. An LLM can help interpret varied wording, but must cite the catalog and must not reserve or disclose protected records without authorization.

Teaching examples are illustrative unless an external source or real model measurement is explicitly identified.

A misconception to avoid

High engagement or polished demos alone do not prove a system is beneficial. Claims like 'reduces cost' or 'improves accuracy' need measurement in the actual application.

Check your understanding

Think first, then open each answer to check your explanation. These self-checks do not record a score.

1. Should a simple deterministic solution be ruled out because the project is called AI?

No. Choose the simplest method that meets evaluated requirements.

2. What should be measured before declaring the pilot successful?

At minimum task correctness, failure modes, user effort and cost against a baseline.

Try it yourself

Complete a one-page project canvas: problem, users, baseline, AI necessity, data, architecture, failure modes, approval, five test cases and a go/no-go decision.

Explain it back: Summarize the main idea in your own words and name one limitation or risk.

Continue learning

Read an independent reference: https://www.nist.gov/itl/ai-risk-management-framework. This link is for reference, not a requirement to create an account. External websites may have different privacy and access policies.

Educational pilot. Terminology, age suitability and activities are subject to expert review. These pages do not send your answers to a server.