Lesson 4 of 12

Structured learning draft

Cleaning with LLMs

In Natural Language Processing, the way a learner handles cleaning shapes how LLMs is used and evaluated. Cleaning resolves invalid, missing, duplicated or inconsistent observations. This advanced lesson focuses on a decision or output that another person can inspect.

Learning objectives

Explain cleaning in the context of Natural Language Processing.
Apply LLMs to a bounded practical task.
Evaluate the result using explicit quality criteria.

Instructional figureContinuous cycle

Cleaning: from context to evidence

Cleaning connects question and source data to a checked finding in Natural Language Processing.

Question and source data

Define the purpose, intended user and LLMs constraints.

frames

Cleaning

Profile first, state rules and retain an audit of changes.

produces evidence for

Checked finding

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

Review the evidence, adjust the method, and repeat.

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

Cleaning resolves invalid, missing, duplicated or inconsistent observations. For LLMs, distinguish performing an operation from demonstrating that it suits the stated purpose. Profile first, state rules and retain an audit of changes. Record assumptions that could change the conclusion.

Apply cleaning deliberately

State the Natural Language Processing task and the decision it supports.
Prepare a small LLMs case with a known input and difficult boundary.
Profile first, state rules and retain an audit of changes.
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 LLMs evidence path

A four-step worked example for applying cleaning to LLMs, including a boundary test and revision.

Known input

Preserve the original LLMs case and expected result.

test

Normal test

Confirm the basic path behaves as expected.

challenge

Boundary test

Expose an assumption in the cleaning 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.

cleaning evidence record
Review point	Evidence
Purpose	The specific LLMs outcome and intended user
Method	The cleaning 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 LLMs before defining what cleaning must achieve.
Checking only the easiest Natural Language Processing example.
Reporting a result without its input, assumptions or limitation.

Practice activity

Apply the lesson

For Natural Language Processing, complete a bounded LLMs task demonstrating cleaning. 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 Natural Language Processing, which evidence best supports a cleaning result produced with LLMs?

A polished LLMs 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 Natural Language Processing, cleaning means: Cleaning resolves invalid, missing, duplicated or inconsistent observations.
A credible LLMs result includes a checked boundary, not only a successful example.
The next lesson builds on this cleaning evidence record.

Sources and further reading

The Python TutorialPython Software Foundation - accessed 2026-08-21
User Guidescikit-learn - accessed 2026-08-21

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