Lesson 1 of 12

Structured learning draft

Problem discovery with Shopify

In E-Commerce with AI Tools, the way a learner handles problem discovery shapes how Shopify is used and evaluated. Problem discovery tests whether a recurring costly situation exists. This beginner lesson focuses on a decision or output that another person can inspect.

Learning objectives

Explain problem discovery in the context of E-Commerce with AI Tools.
Apply Shopify to a bounded practical task.
Evaluate the result using explicit quality criteria.

Instructional figureProcess flow

Problem Discovery: from context to evidence

Problem Discovery connects customer evidence to a measured outcome in E-Commerce with AI Tools.

Customer evidence

Define the purpose, intended user and Shopify constraints.

frames

Problem Discovery

Ask about recent behaviour and evidence, not hypothetical enthusiasm.

produces evidence for

Measured outcome

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

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

Problem discovery tests whether a recurring costly situation exists. For Shopify, distinguish performing an operation from demonstrating that it suits the stated purpose. Ask about recent behaviour and evidence, not hypothetical enthusiasm. Record assumptions that could change the conclusion.

Apply problem discovery deliberately

State the E-Commerce with AI Tools task and the decision it supports.
Prepare a small Shopify case with a known input and difficult boundary.
Ask about recent behaviour and evidence, not hypothetical enthusiasm.
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 Shopify evidence path

A four-step worked example for applying problem discovery to Shopify, including a boundary test and revision.

Known input

Preserve the original Shopify case and expected result.

test

Normal test

Confirm the basic path behaves as expected.

challenge

Boundary test

Expose an assumption in the problem discovery 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.

problem discovery evidence record
Review point	Evidence
Purpose	The specific Shopify outcome and intended user
Method	The problem discovery 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 Shopify before defining what problem discovery must achieve.
Checking only the easiest E-Commerce with AI Tools example.
Reporting a result without its input, assumptions or limitation.

Practice activity

Apply the lesson

For E-Commerce with AI Tools, complete a bounded Shopify task demonstrating problem discovery. 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 E-Commerce with AI Tools, which evidence best supports a problem discovery result produced with Shopify?

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

Lesson summary

For E-Commerce with AI Tools, problem discovery means: Problem discovery tests whether a recurring costly situation exists.
A credible Shopify result includes a checked boundary, not only a successful example.
The next lesson builds on this problem discovery evidence record.

Sources and further reading

Business GuideU.S. Small Business Administration - accessed 2026-08-21
SME and Entrepreneurship PolicyOECD - accessed 2026-08-21

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Problem discovery with Shopify

Learning objectives

Explain problem discovery in the context of E-Commerce with AI Tools.
Apply Shopify to a bounded practical task.
Evaluate the result using explicit quality criteria.

Instructional figureProcess flow

Problem Discovery: from context to evidence

Problem Discovery connects customer evidence to a measured outcome in E-Commerce with AI Tools.

Customer evidence

Define the purpose, intended user and Shopify constraints.

frames

Problem Discovery

Ask about recent behaviour and evidence, not hypothetical enthusiasm.

produces evidence for

Measured outcome

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

Apply problem discovery deliberately

State the E-Commerce with AI Tools task and the decision it supports.
Prepare a small Shopify case with a known input and difficult boundary.
Ask about recent behaviour and evidence, not hypothetical enthusiasm.
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 Shopify evidence path

A four-step worked example for applying problem discovery to Shopify, including a boundary test and revision.

Known input

Preserve the original Shopify case and expected result.

test

Normal test

Confirm the basic path behaves as expected.

challenge

Boundary test

Expose an assumption in the problem discovery 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.

problem discovery evidence record
Review point	Evidence
Purpose	The specific Shopify outcome and intended user
Method	The problem discovery 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 Shopify before defining what problem discovery must achieve.
Checking only the easiest E-Commerce with AI Tools example.
Reporting a result without its input, assumptions or limitation.

Practice activity

Apply the lesson

Check your understanding

In E-Commerce with AI Tools, which evidence best supports a problem discovery result produced with Shopify?

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

Lesson summary

For E-Commerce with AI Tools, problem discovery means: Problem discovery tests whether a recurring costly situation exists.
A credible Shopify result includes a checked boundary, not only a successful example.
The next lesson builds on this problem discovery evidence record.

Sources and further reading

Business GuideU.S. Small Business Administration - accessed 2026-08-21
SME and Entrepreneurship PolicyOECD - accessed 2026-08-21

Personal study note

Your notes

Saved on this device

Notes for this lesson

0 / 12,000No saved note yet.