Lesson 4 of 12

Structured learning draft

Clear instructions with Sora

In AI Video: Sora & RunwayML, the way a learner handles clear instructions shapes how Sora is used and evaluated. A clear instruction names task, audience, constraints, format and quality test. This beginner lesson focuses on a decision or output that another person can inspect.

Learning objectives

Explain clear instructions in the context of AI Video: Sora & RunwayML.
Apply Sora to a bounded practical task.
Evaluate the result using explicit quality criteria.

Instructional figureContinuous cycle

Clear Instructions: from context to evidence

Clear Instructions connects bounded input to a reviewed output in AI Video: Sora & RunwayML.

Bounded input

Define the purpose, intended user and Sora constraints.

frames

Clear Instructions

Rewrite a vague request as a bounded specification, then compare outputs.

produces evidence for

Reviewed output

Compare the observed result with a normal case, boundary case and stated limitation.

Review the evidence, adjust the method, and repeat.

Takeaway: Clear Instructions is credible only when the result can be traced back to its purpose, inputs and constraints. Clear Instructions is the decision layer between the starting context and evidence that the result is fit for purpose.

A clear instruction names task, audience, constraints, format and quality test. For Sora, distinguish performing an operation from demonstrating that it suits the stated purpose. Rewrite a vague request as a bounded specification, then compare outputs. Record assumptions that could change the conclusion.

Apply clear instructions 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.
Rewrite a vague request as a bounded specification, then compare outputs.
Compare the observed result with the expected behaviour and explain differences.
Save the evidence, limitation and next action in a review record.

Instructional figureProcess flow

A worked Sora evidence path

A four-step worked example for applying clear instructions to Sora, including a boundary test and revision.

Known input

Preserve the original Sora case and expected result.

test

Normal test

Confirm the basic path behaves as expected.

challenge

Boundary test

Expose an assumption in the clear instructions method.

improve

Revision

Change the method, rerun both cases and record the limitation.

Takeaway: A successful normal case is not enough; the difficult case must influence the final method. The boundary result changes the method before the evidence is accepted.

clear instructions evidence record
Review point	Evidence
Purpose	The specific Sora outcome and intended user
Method	The clear instructions 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 clear instructions 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 clear instructions. 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 clear instructions result produced with Sora?

A polished Sora output without its input A screenshot showing the tool opened A claim that the method always works The input, expected behaviour, normal and boundary results, and a limitation

Lesson summary

For AI Video: Sora & RunwayML, clear instructions means: A clear instruction names task, audience, constraints, format and quality test.
A credible Sora result includes a checked boundary, not only a successful example.
The next lesson builds on this clear instructions evidence record.

Sources and further reading

AI Risk Management Framework 1.0NIST - accessed 2026-08-21
AI PrinciplesOECD - accessed 2026-08-21

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