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Module 04 · Client-Ready Deliverables

Handling Revision Requests Efficiently With AI Variants

20 minfocused lesson7practical steps4grounded questions4source links
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On this lesson

Course outline

Revision work becomes expensive when feedback is vague, contradictory, or detached from the approved brief. AI can classify notes and generate bounded copy or layout alternatives, but it cannot decide what the client meant or quietly expand the contract.

In this lesson you will turn mixed feedback into a revision register, resolve ambiguity, produce controlled variants, and send an evidence-based response. The artifact is a review round that another designer could reproduce.

// concept

Normalize Feedback Before Editing

Gather comments from the agreed source—email, annotated PDF, design comments, or a single meeting note—and give every request an ID. Do not begin editing from scattered WhatsApp messages.

Classify each item:

ClassMeaningAction
CorrectionWrong name, date, spelling, factual detailVerify source and fix
Brief fitDesign misses an agreed requirementRevise against the criterion
PreferenceSubjective choice inside scopeOffer bounded choice if needed
New scopeNew format, concept, language, or assetEstimate and approve first
ConflictTwo notes cannot both be satisfiedAsk the decision owner
Unclear“Make it pop” or similarTranslate into a concrete question

Use AI only after removing confidential material and preserving the original notes:

// prompt — copy me7 lines
Classify each feedback item as correction, brief fit, preference, new scope,
conflict, or unclear. Quote the item ID, do not rewrite the client's words,
and explain the classification in one sentence. Do not decide ambiguous items.
Flag any request that conflicts with the approved brief excerpt.

APPROVED BRIEF: [minimum relevant excerpt]
FEEDBACK ITEMS: [numbered, redacted list]

Review every classification. AI does not know your contract, decision maker, or prior verbal approvals.

// concept

Convert Vague Notes Into Tests

Respond to “make the logo bigger” with context: “Is the issue recognition in the mobile header, or do you want more visual emphasis across all layouts?” Respond to “premium” by showing which approved attributes, audience cues, and production choices it should affect.

For each accepted request, record:

// prompt — copy me6 lines
R-04 / Source: client email 19 Jul
Request: Increase event-date prominence on mobile announcement
Change: Date moves above body; size 18 → 24; contrast pair unchanged
Acceptance test: date is the second read after title at 390px width
Files affected: IG portrait, WhatsApp status
Status: ready for review

An acceptance test prevents endless “better” loops. It also makes it clear when a request has been met.

// concept

Generate Bounded Variants

Variants answer one question at a time. Duplicate the last approved source, change one controlled variable, and label A/B/C. If you change type, color, layout, and wording together, the client cannot tell what improved the result.

AI is especially useful for constrained copy length:

// prompt — copy me5 lines
Write three headline variants for this approved message. Maximum 32 characters
including spaces. Preserve the date and the claim exactly. Tone: direct and
helpful. Do not add urgency, discounts, guarantees, or facts.

APPROVED MESSAGE: Free sample orientation on 24 July; registration required.

Check character count and facts yourself. For visual variants, use the same approved content and assets, then vary only the stated design decision. Keep a source-file branch or duplicate for each review round so rejected experiments never overwrite approved work.

// worked_example

Worked Example

The designer creates a register. Two spelling corrections are applied immediately after checking the approved course list. “Youthful” and “formal” are marked as conflict and sent to the named decision owner with two neutral sample directions. The Urdu version is marked new scope, with an estimate for translation review, typography adjustment, and new exports.

For the accepted request “offer is hard to find,” the designer makes two variants:

  • A: offer moves into a high-contrast band; all other elements remain fixed.
  • B: offer remains in place but increases one type step; all else remains fixed.

The client chooses A because it improves the stated scan order. The response deck lists R-01 through R-09, before/after evidence, unresolved decisions, and the exact files included. No one has to reconstruct the round from voice notes.

// failure_cases

Failure Cases to Diagnose

7 cases to diagnose

  • Editing begins from scattered messages

    consolidate and number the authoritative feedback first.

  • AI paraphrases away the client’s intent

    retain the original quote beside every classification.

  • A variant changes several variables

    return to the last approved source and isolate one decision.

  • New scope is delivered for free by accident

    pause and obtain written approval of impact, price, and timing.

  • Two stakeholders conflict

    ask the named decision owner rather than blending incompatible notes.

  • A correction introduces another error

    verify names, numbers, dates, and URLs against the approved source.

  • Rejected files are called final

    use deterministic version names and a visible approval status.

// pakistan_angle

Pakistan Angle

WhatsApp is often the fastest approval channel, but voice notes, forwarded screenshots, and mixed-language comments can erase context. Summarize each call or voice note into a numbered register and ask the authorized decision maker to confirm it in writing. Do not upload private client chats, customer phone numbers, invoices, or CNIC-linked material to an AI tool.

For bilingual revisions, “make an Urdu version” is not a copy-paste task. It may require translation approval, a different font, increased line height, right-to-left layout, and fresh mobile/print checks. Treat that effort honestly in scope. If load-shedding interrupts uploads, keep local timestamped sources and send lightweight proof files before full packages.

// hands_on

Hands-On Exercise

7 steps

  1. Collect one realistic set of at least eight feedback notes from a project or labelled simulation.

  2. Number and classify them with the six classes in this lesson.

  3. Write clarification questions for every unclear or conflicting item.

  4. Convert five accepted requests into change statements and acceptance tests.

  5. Create an A/B pair that isolates one visual variable and three AI-assisted copy variants under a hard length limit.

  6. Verify every fact, record scope changes separately, and package a response summary.

  7. Ask a reviewer to trace each exported change back to its request ID.

// completion_rubric

Completion Rubric

6 checks — tick as you verify

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// sources

Sources

4 official sources — check every claim yourself

// check_yourself

Check yourself

4 questions · answers and options are taken word-for-word from this course

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  1. 1 / 4 · diagnose

    Your work shows this failure mode: “AI paraphrases away the client’s intent.” What does the lesson tell you to do about it?