Lesson 1 of 12
Structured learning draftCapabilities with Sora
In AI Video: Sora & RunwayML, the way a learner handles capabilities shapes how Sora is used and evaluated. A capability is a task an AI system can perform under stated conditions, not a universal guarantee. This beginner lesson focuses on a decision or output that another person can inspect.
Learning objectives
- Explain capabilities in the context of AI Video: Sora & RunwayML.
- Apply Sora to a bounded practical task.
- Evaluate the result using explicit quality criteria.
Capabilities: from context to evidence
Capabilities connects bounded input to a reviewed output in AI Video: Sora & RunwayML.
Define the purpose, intended user and Sora constraints.
Compare representative inputs and record where performance changes.
Compare the observed result with a normal case, boundary case and stated limitation.
A capability is a task an AI system can perform under stated conditions, not a universal guarantee. For Sora, distinguish performing an operation from demonstrating that it suits the stated purpose. Compare representative inputs and record where performance changes. Record assumptions that could change the conclusion.
Apply capabilities deliberately
- State the AI Video: Sora & RunwayML task and the decision it supports.
- Prepare a small Sora case with a known input and difficult boundary.
- Compare representative inputs and record where performance changes.
- Compare the observed result with the expected behaviour and explain differences.
- Save the evidence, limitation and next action in a review record.
A worked Sora evidence path
A four-step worked example for applying capabilities to Sora, including a boundary test and revision.
Preserve the original Sora case and expected result.
Confirm the basic path behaves as expected.
Expose an assumption in the capabilities method.
Change the method, rerun both cases and record the limitation.
| Review point | Evidence |
|---|---|
| Purpose | The specific Sora outcome and intended user |
| Method | The capabilities decision, input and version or context |
| Result | Observed output plus a checked boundary case |
| Limitation | What the result does not establish and the next safe action |
Common mistakes
- Using Sora before defining what capabilities must achieve.
- Checking only the easiest AI Video: Sora & RunwayML example.
- Reporting a result without its input, assumptions or limitation.
Practice activity
Apply the lesson
For AI Video: Sora & RunwayML, complete a bounded Sora task demonstrating capabilities. Keep the original input, numbered method, normal test, boundary test, observed results and a 100-word self-review naming one limitation and next improvement.
Check your understanding
In AI Video: Sora & RunwayML, which evidence best supports a capabilities result produced with Sora?
Lesson summary
- For AI Video: Sora & RunwayML, capabilities means: A capability is a task an AI system can perform under stated conditions, not a universal guarantee.
- A credible Sora result includes a checked boundary, not only a successful example.
- The next lesson builds on this capabilities evidence record.
Sources and further reading
- AI Risk Management Framework 1.0NIST - accessed 2026-08-21
- AI PrinciplesOECD - accessed 2026-08-21
Personal study note