Zior AI

If an image is destined for a vertical story, do not generate a square and hope the crop works.

Use this Midjourney placement-first workflow:

1. Choose the final placement before writing the prompt: square feed, vertical story, widescreen hero, or print.

2. Write a short prompt with the subject, medium, environment, lighting, color, mood, and composition.

3. Leave deliberate negative space for the real headline and call to action; add that copy later in a design tool.

4. Generate in the target shape with the aspect-ratio parameter, such as --ar 9:16 for a vertical concept.

5. Create variations by changing one variable at a time so you can tell what improved the result.

6. Inspect the chosen image closely for distorted details, accidental text, logos, or visual claims.

7. Add approved brand assets and factual copy outside the generated image.

8. Export at the required dimensions and review the final crop on the destination device.

Practical use case: create a vertical background concept for an event story while preserving clear space for the event name, date, and signup button.

Midjourney documents that --ar controls the width-to-height ratio, not the final pixel dimensions. It also recommends clear, focused prompts and provides --no for exclusions. Always review the output and confirm you have the rights to use every reference and brand element.

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An automation is not finished when the happy path works. It is finished when bad input fails safely.

Use this Zapier/Make failure-mode drill before switching a workflow on:

1. List four realistic failures: a missing required field, a duplicate trigger, an expired connection, and a temporary app outage.

2. Put validation or filtering before any irreversible action such as sending a message or creating a customer record.

3. Run a clearly labeled test record through the successful path.

4. Deliberately submit one bad sample for each failure on your list.

5. Decide the safe response: pause for human review, retry a temporary error, or skip only when losing that item is acceptable.

6. Log the source record ID, run time, failed step, and error message.

7. Test recovery and confirm that retrying does not send a duplicate email or create a duplicate record.

8. Turn the workflow on gradually and review its run history after the first live inputs.

Practical use case: protect a lead-form-to-CRM workflow from creating incomplete contacts or sending duplicate follow-ups.

Make documents error handlers, incomplete executions, and retry options for handling failed scenario runs. The exact controls differ between automation platforms, so confirm the behavior in your own account and never assume a retry is harmless.

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In transcript-based editing, fix the words before you start cutting the story.

Use this Descript rough-cut workflow:

1. Import the recording and let the transcript finish processing.

2. Correct names, technical terms, dates, and numbers before making structural edits.

3. Check the speaker labels so every quote is attributed to the right person.

4. Read the transcript once and mark the one outcome the finished piece must deliver.

5. Remove repeated explanations and off-topic sections by editing the text.

6. Preview every edit boundary with sound; restore anything that changes the meaning or creates an unnatural jump.

7. Add captions only after the wording and order are locked, then check line breaks on a phone-sized preview.

8. Watch and listen to the complete export once before publishing.

Practical use case: turn a 20-minute customer interview into a focused three-minute FAQ video while preserving the speaker’s intended meaning.

Descript documents that its transcript is linked to the underlying media, so deleting or moving text also edits the audio or video. Its captions follow the script and update with edits. That speed makes the final human review especially important.

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An AI research answer is a starting point—not evidence until its citations survive a click.

Use this Perplexity source-check workflow:

1. Ask one focused question with the market, location, and time period clearly defined.

2. Request a table with each key claim, publication date, source name, and direct citation.

3. Open every citation that affects your decision; confirm that the source actually supports the claim.

4. Prefer primary sources for product features, prices, laws, company statements, and statistics.

5. Mark conflicting figures and missing dates instead of averaging them together.

6. Run a narrower follow-up search only for the unresolved gaps.

7. Write the final brief in two sections: verified facts and your own interpretation.

Practical use case: prepare a one-page competitor brief before a client meeting without confusing a polished summary with verified research.

Perplexity says its answers include citations and links to original sources. Those links make verification easier, but you still need to inspect them and confirm important details before using the result.

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A resized design is a draft, not a finished campaign asset.

Use this Canva cross-format quality check:

1. Build one master design with a clear headline, supporting detail, and one call to action.

2. Duplicate it for each channel, or use Magic Switch to resize it if that feature is available on your plan.

3. Check every version for cropped faces, awkward line breaks, tiny text, and elements sitting too close to the edge.

4. Reposition the main subject instead of simply shrinking the entire layout.

5. Keep the message hierarchy consistent, but remove nonessential details from smaller formats.

6. Export one sample of each size and review it on the device where people will see it.

7. Save the approved versions together so the next campaign starts from a tested system.

Practical use case: turn one event announcement into a square feed post, vertical story, and presentation slide while keeping the message readable in every format.

Canva says Magic Switch can resize one design for multiple channels; exact feature availability depends on the plan and editor. Always review generated or resized outputs before publishing.

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Numbers that look clear on a page can sound wrong in an AI voiceover.

Use this ElevenLabs speech-normalization check before generating the full script:

1. Highlight every phone number, price, date, time, address, URL, measurement, and abbreviation.

2. Rewrite each one exactly as it should be spoken—for example, decide whether “2026” means “twenty twenty-six” or “two thousand twenty-six.”

3. Put the difficult lines into a short test passage with the sentences immediately before and after them.

4. Generate the sample and review it on headphones and a phone speaker.

5. Fix only the words or punctuation causing the issue, then test again.

6. Save the approved spoken version in the script before producing the full narration.

7. Listen to the final audio once more against the written source.

Practical use case: make a product demo, event announcement, or onboarding lesson read prices, dates, and contact details consistently.

Only use voices you are authorized to use, and verify every factual detail before publishing.

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Most problems with an existing custom GPT are instruction problems—not a reason to add more tools.

Before sharing one, run this five-case test:

1. Give it a normal request it should handle.

2. Remove one required detail and check whether it asks a useful follow-up.

3. Provide conflicting source material and see whether it flags the conflict.

4. Ask for something outside its scope and check that it sets a clear boundary.

5. Request the correct answer in an unusual format.

Score every result on accuracy, source use, tone, format, and whether it admits missing information. Tighten the instructions and add examples before enabling another capability.

Practical use case: test an existing brief-review, onboarding, or internal FAQ GPT before teammates rely on it.

Keep behavior and workflow rules in Instructions; use Knowledge files as reference material. Test changes in Preview.

Availability depends on account and workspace permissions. OpenAI currently limits new GPT creation and publishing to eligible Business, Enterprise, and Edu workspaces, while access to existing GPTs and editing eligibility can differ.

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An AI-avatar video should make sense even when the viewer’s phone is muted.

Use this HeyGen silent-viewing check:

1. Give each scene one idea and one clear visual purpose.

2. Add stylized captions in AI Studio and match them against the final script.

3. Keep lines short enough to read comfortably; fix awkward breaks around names and numbers.

4. Use a high-contrast caption style that stays clear over the background.

5. Watch the complete video once with sound off on a phone-sized screen.

6. Watch it again with sound on to check pacing, pronunciation, and caption timing.

7. Verify every product claim, date, price, and call to action before publishing.

Practical use case: create a short onboarding or FAQ video that still communicates the answer when members watch silently.

Use only avatars and voices you are authorized to use. Creating a Digital Twin of another person requires that person’s own consent through HeyGen’s process.

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Don’t remove every “um” from a recording. Clean audio should still sound human.

Try this Descript filler-word review:

1. Correct names and transcription mistakes before editing the media.

2. Open the filler-word tool and preview each detected word in context.

3. Delete only the fillers that distract from the speaker’s meaning or pacing.

4. Use the option to avoid harsh cuts when it is available, then listen across every edit.

5. Keep pauses or conversational words that preserve emotion and clarity.

6. Review the final captions and play the export once at normal speed before publishing.

Practical use case: tighten a customer interview or course lesson without turning the speaker into a rushed, unnatural voice.

Descript can find filler words and edit audio or video through its transcript, but the final pacing still needs a human ear.

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Perplexity is most useful when you treat its citations as a research queue—not automatic proof.

Use this quick source-audit workflow:

1. Write the exact claim or decision you need to check.

2. Ask Perplexity for a current answer with a clear date range and a preference for primary sources.

3. Open every citation supporting an important claim.

4. Record the publisher, publication date, and the specific evidence each source provides.

5. Search for one credible source that challenges the initial answer.

6. Label each conclusion verified, uncertain, or outdated before sharing it.

Practical use case: check a competitor’s current features, policies, or positioning before adding them to a client brief.

Perplexity provides linked citations, but you still need to confirm that each source is current and genuinely supports the claim.

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