August 30, 2026AIExplore

How to Use Figma AI to Replace Placeholder Content

Swap lorem ipsum and duplicate strings for unique realistic content across selected frames so reviews feel credible without rewriting every card by hand.

Figma AI

Replace content with Figma AI swaps placeholder or duplicate text for unique realistic content across selected frames. It is ideal when mocks still show lorem ipsum or identical card titles. Combine with rewrite tools for tone and with Rename layers before handoff. Overview: /explore/figma-ai.

This guide covers selection scope, domain briefs, and verification of invented details. Pair with text tools at /blog/how-to-use-figma-ai-to-rewrite-translate-and-shorten-text.

When Replace content is the right job

Use it when structure is done and the file still looks fake because every card repeats. Do not use it to overwrite approved marketing or production design system documentation.

Step by step workflow

1. Select frames with placeholder or duplicate copy

Exclude locked marketing and legal layers from the selection.

Scenario:
A realistic figma-ai workflow is being prepared for Use Figma AI for First Draft Explorations using figma-ai. The starting requirement is: Selection Frames: dashboard metric cards Excluded: footer legal, nav labels Goal: realistic SaaS sample data. The work should be tested on a representative input first so the result can be reviewed before the same process is applied to the full project or production workload.

Objective:
Create a production-ready result for "Selection Frames: dashboard metric cards Excluded: footer legal, nav labels Goal: realistic SaaS sample data" that directly supports the article use case. The output should be specific, reviewable, and reproducible rather than a generic demonstration. Keep the requested goal as the primary outcome and avoid adding unrelated features, assumptions, or unsupported capabilities.

Inputs:
Use the actual source material required for this task, together with the intended audience, destination, format, and quality requirements. Specify relevant files, text, URLs, structured data, reference assets, dimensions, language, tone, timing, model or generation settings, credentials, field mappings, or integration values when they apply. Keep secrets out of the example and use the product's supported credential or configuration mechanism.

Workflow:
1. Start with one representative input that contains the important characteristics of the real workload.
2. Configure figma-ai specifically for the requested task and make the important settings explicit.
3. Run the first pass and inspect the result against the objective.
4. Correct only the settings or source material responsible for a failed requirement instead of changing everything at once.
5. Validate the intermediate result before passing it to the next step.
6. When the result meets the requirements, save the successful configuration so the same process can be repeated consistently.
7. If this is a multi-step workflow, verify each handoff and keep a clear fallback or review path for failures.

Requirements:
The example must use supported functionality only. Do not invent product features, pricing, limits, integrations, model names, API behavior, or unavailable settings. Preserve important source data and formatting. Validate required fields before processing, handle empty or invalid input explicitly, prevent accidental duplicate processing where relevant, and stop or route the item for review when a required step fails. For credentials, use the supported secure configuration rather than placing secrets directly in the content.

Expected output:
The final result should directly satisfy the "Selection Frames: dashboard metric cards Excluded: footer legal, nav labels Goal: realistic SaaS sample data" requirement and be understandable without guessing what was supplied or configured. A successful run should produce the intended output in the requested format, with the important values and workflow decisions clear enough to reproduce the result. If the workflow can fail, the expected behavior should also make the failure visible and provide a clear next action instead of silently producing an incomplete result.

2. Run Replace content with domain context

Tell Figma the product domain and what must stay placeholder such as emails or IDs.

Scenario:
A realistic figma-ai workflow is being prepared for Use Figma AI for First Draft Explorations using figma-ai. The starting requirement is: Brief Domain: B2B analytics Need: unique metric labels and values Locks: no company names, no real customer data. The work should be tested on a representative input first so the result can be reviewed before the same process is applied to the full project or production workload.

Objective:
Create a production-ready result for "Brief Domain: B2B analytics Need: unique metric labels and values Locks: no company names, no real customer data" that directly supports the article use case. The output should be specific, reviewable, and reproducible rather than a generic demonstration. Keep the requested goal as the primary outcome and avoid adding unrelated features, assumptions, or unsupported capabilities.

Expected output:
The final result should directly satisfy the "Brief Domain: B2B analytics Need: unique metric labels and values Locks: no company names, no real customer data" requirement and be understandable without guessing what was supplied or configured. A successful run should produce the intended output in the requested format, with the important values and workflow decisions clear enough to reproduce the result. If the workflow can fail, the expected behavior should also make the failure visible and provide a clear next action instead of silently producing an incomplete result.

3. Verify plausibility

Fix impossible metrics manually. Translate later if the prototype is localized.

Scenario:
A realistic figma-ai workflow is being prepared for Use Figma AI for First Draft Explorations using figma-ai. The starting requirement is: QA [ ] Values look plausible [ ] No repeated titles left [ ] Approved headlines untouched [ ] Sample data labeled for stakeholders. The work should be tested on a representative input first so the result can be reviewed before the same process is applied to the full project or production workload.

Objective:
Create a production-ready result for "QA [ ] Values look plausible [ ] No repeated titles left [ ] Approved headlines untouched [ ] Sample data labeled for stakeholders" that directly supports the article use case. The output should be specific, reviewable, and reproducible rather than a generic demonstration. Keep the requested goal as the primary outcome and avoid adding unrelated features, assumptions, or unsupported capabilities.

Expected output:
The final result should directly satisfy the "QA [ ] Values look plausible [ ] No repeated titles left [ ] Approved headlines untouched [ ] Sample data labeled for stakeholders" requirement and be understandable without guessing what was supplied or configured. A successful run should produce the intended output in the requested format, with the important values and workflow decisions clear enough to reproduce the result. If the workflow can fail, the expected behavior should also make the failure visible and provide a clear next action instead of silently producing an incomplete result.

Practical Replace content briefs

SaaS metrics

Scenario:
A realistic figma-ai workflow is being prepared for Use Figma AI for First Draft Explorations using figma-ai. The starting requirement is: Replace content in these three dashboard cards with realistic SaaS metrics labels and values. No company names.. The work should be tested on a representative input first so the result can be reviewed before the same process is applied to the full project or production workload.

Objective:
Create a production-ready result for "Replace content in these three dashboard cards with realistic SaaS metrics labels and values. No company names." that directly supports the article use case. The output should be specific, reviewable, and reproducible rather than a generic demonstration. Keep the requested goal as the primary outcome and avoid adding unrelated features, assumptions, or unsupported capabilities.

Expected output:
The final result should directly satisfy the "Replace content in these three dashboard cards with realistic SaaS metrics labels and values. No company names." requirement and be understandable without guessing what was supplied or configured. A successful run should produce the intended output in the requested format, with the important values and workflow decisions clear enough to reproduce the result. If the workflow can fail, the expected behavior should also make the failure visible and provide a clear next action instead of silently producing an incomplete result.

Fitness app

Scenario:
A realistic figma-ai workflow is being prepared for Use Figma AI for First Draft Explorations using figma-ai. The starting requirement is: Replace workout cards with unique workout names, durations, and difficulty. Friendly tone.. The work should be tested on a representative input first so the result can be reviewed before the same process is applied to the full project or production workload.

Objective:
Create a production-ready result for "Replace workout cards with unique workout names, durations, and difficulty. Friendly tone." that directly supports the article use case. The output should be specific, reviewable, and reproducible rather than a generic demonstration. Keep the requested goal as the primary outcome and avoid adding unrelated features, assumptions, or unsupported capabilities.

Expected output:
The final result should directly satisfy the "Replace workout cards with unique workout names, durations, and difficulty. Friendly tone." requirement and be understandable without guessing what was supplied or configured. A successful run should produce the intended output in the requested format, with the important values and workflow decisions clear enough to reproduce the result. If the workflow can fail, the expected behavior should also make the failure visible and provide a clear next action instead of silently producing an incomplete result.

Ecommerce list

Scenario:
A realistic figma-ai workflow is being prepared for Use Figma AI for First Draft Explorations using figma-ai. The starting requirement is: Replace product cards with unique product names and prices in USD. No brand trademarks.. The work should be tested on a representative input first so the result can be reviewed before the same process is applied to the full project or production workload.

Objective:
Create a production-ready result for "Replace product cards with unique product names and prices in USD. No brand trademarks." that directly supports the article use case. The output should be specific, reviewable, and reproducible rather than a generic demonstration. Keep the requested goal as the primary outcome and avoid adding unrelated features, assumptions, or unsupported capabilities.

Expected output:
The final result should directly satisfy the "Replace product cards with unique product names and prices in USD. No brand trademarks." requirement and be understandable without guessing what was supplied or configured. A successful run should produce the intended output in the requested format, with the important values and workflow decisions clear enough to reproduce the result. If the workflow can fail, the expected behavior should also make the failure visible and provide a clear next action instead of silently producing an incomplete result.

Inbox

Scenario:
A realistic figma-ai workflow is being prepared for Use Figma AI for First Draft Explorations using figma-ai. The starting requirement is: Replace inbox rows with unique senders and subject lines for a project management app.. The work should be tested on a representative input first so the result can be reviewed before the same process is applied to the full project or production workload.

Objective:
Create a production-ready result for "Replace inbox rows with unique senders and subject lines for a project management app." that directly supports the article use case. The output should be specific, reviewable, and reproducible rather than a generic demonstration. Keep the requested goal as the primary outcome and avoid adding unrelated features, assumptions, or unsupported capabilities.

Expected output:
The final result should directly satisfy the "Replace inbox rows with unique senders and subject lines for a project management app." requirement and be understandable without guessing what was supplied or configured. A successful run should produce the intended output in the requested format, with the important values and workflow decisions clear enough to reproduce the result. If the workflow can fail, the expected behavior should also make the failure visible and provide a clear next action instead of silently producing an incomplete result.

CRM table

Scenario:
A realistic figma-ai workflow is being prepared for Use Figma AI for First Draft Explorations using figma-ai. The starting requirement is: Replace table rows with realistic lead names, stages, and owners. Use clearly fictional names.. The work should be tested on a representative input first so the result can be reviewed before the same process is applied to the full project or production workload.

Objective:
Create a production-ready result for "Replace table rows with realistic lead names, stages, and owners. Use clearly fictional names." that directly supports the article use case. The output should be specific, reviewable, and reproducible rather than a generic demonstration. Keep the requested goal as the primary outcome and avoid adding unrelated features, assumptions, or unsupported capabilities.

Expected output:
The final result should directly satisfy the "Replace table rows with realistic lead names, stages, and owners. Use clearly fictional names." requirement and be understandable without guessing what was supplied or configured. A successful run should produce the intended output in the requested format, with the important values and workflow decisions clear enough to reproduce the result. If the workflow can fail, the expected behavior should also make the failure visible and provide a clear next action instead of silently producing an incomplete result.

Education

Scenario:
A realistic figma-ai workflow is being prepared for Use Figma AI for First Draft Explorations using figma-ai. The starting requirement is: Replace course cards with unique course titles and week counts for a design school catalog.. The work should be tested on a representative input first so the result can be reviewed before the same process is applied to the full project or production workload.

Objective:
Create a production-ready result for "Replace course cards with unique course titles and week counts for a design school catalog." that directly supports the article use case. The output should be specific, reviewable, and reproducible rather than a generic demonstration. Keep the requested goal as the primary outcome and avoid adding unrelated features, assumptions, or unsupported capabilities.

Expected output:
The final result should directly satisfy the "Replace course cards with unique course titles and week counts for a design school catalog." requirement and be understandable without guessing what was supplied or configured. A successful run should produce the intended output in the requested format, with the important values and workflow decisions clear enough to reproduce the result. If the workflow can fail, the expected behavior should also make the failure visible and provide a clear next action instead of silently producing an incomplete result.

Support tickets

Scenario:
A realistic figma-ai workflow is being prepared for Use Figma AI for First Draft Explorations using figma-ai. The starting requirement is: Replace ticket list with unique issue titles and priorities. Neutral tone.. The work should be tested on a representative input first so the result can be reviewed before the same process is applied to the full project or production workload.

Objective:
Create a production-ready result for "Replace ticket list with unique issue titles and priorities. Neutral tone." that directly supports the article use case. The output should be specific, reviewable, and reproducible rather than a generic demonstration. Keep the requested goal as the primary outcome and avoid adding unrelated features, assumptions, or unsupported capabilities.

Expected output:
The final result should directly satisfy the "Replace ticket list with unique issue titles and priorities. Neutral tone." requirement and be understandable without guessing what was supplied or configured. A successful run should produce the intended output in the requested format, with the important values and workflow decisions clear enough to reproduce the result. If the workflow can fail, the expected behavior should also make the failure visible and provide a clear next action instead of silently producing an incomplete result.

Calendar

Scenario:
A realistic figma-ai workflow is being prepared for Use Figma AI for First Draft Explorations using figma-ai. The starting requirement is: Replace event titles with unique team meeting names and rooms. No real employee names.. The work should be tested on a representative input first so the result can be reviewed before the same process is applied to the full project or production workload.

Objective:
Create a production-ready result for "Replace event titles with unique team meeting names and rooms. No real employee names." that directly supports the article use case. The output should be specific, reviewable, and reproducible rather than a generic demonstration. Keep the requested goal as the primary outcome and avoid adding unrelated features, assumptions, or unsupported capabilities.

Expected output:
The final result should directly satisfy the "Replace event titles with unique team meeting names and rooms. No real employee names." requirement and be understandable without guessing what was supplied or configured. A successful run should produce the intended output in the requested format, with the important values and workflow decisions clear enough to reproduce the result. If the workflow can fail, the expected behavior should also make the failure visible and provide a clear next action instead of silently producing an incomplete result.

Notifications

Scenario:
A realistic figma-ai workflow is being prepared for Use Figma AI for First Draft Explorations using figma-ai. The starting requirement is: Replace notification copy with unique account events for a banking app. No real balances that look audited.. The work should be tested on a representative input first so the result can be reviewed before the same process is applied to the full project or production workload.

Objective:
Create a production-ready result for "Replace notification copy with unique account events for a banking app. No real balances that look audited." that directly supports the article use case. The output should be specific, reviewable, and reproducible rather than a generic demonstration. Keep the requested goal as the primary outcome and avoid adding unrelated features, assumptions, or unsupported capabilities.

Expected output:
The final result should directly satisfy the "Replace notification copy with unique account events for a banking app. No real balances that look audited." requirement and be understandable without guessing what was supplied or configured. A successful run should produce the intended output in the requested format, with the important values and workflow decisions clear enough to reproduce the result. If the workflow can fail, the expected behavior should also make the failure visible and provide a clear next action instead of silently producing an incomplete result.

Comments

Scenario:
A realistic figma-ai workflow is being prepared for Use Figma AI for First Draft Explorations using figma-ai. The starting requirement is: Replace comment threads with unique short feedback notes for a design review prototype.. The work should be tested on a representative input first so the result can be reviewed before the same process is applied to the full project or production workload.

Objective:
Create a production-ready result for "Replace comment threads with unique short feedback notes for a design review prototype." that directly supports the article use case. The output should be specific, reviewable, and reproducible rather than a generic demonstration. Keep the requested goal as the primary outcome and avoid adding unrelated features, assumptions, or unsupported capabilities.

Expected output:
The final result should directly satisfy the "Replace comment threads with unique short feedback notes for a design review prototype." requirement and be understandable without guessing what was supplied or configured. A successful run should produce the intended output in the requested format, with the important values and workflow decisions clear enough to reproduce the result. If the workflow can fail, the expected behavior should also make the failure visible and provide a clear next action instead of silently producing an incomplete result.

More copyable examples

Scenario:
A realistic figma-ai workflow is being prepared for Use Figma AI for First Draft Explorations using figma-ai. The starting requirement is: Recipes Replace recipe cards with unique dish names and cook times for a cooking app.. The work should be tested on a representative input first so the result can be reviewed before the same process is applied to the full project or production workload.

Objective:
Create a production-ready result for "Recipes Replace recipe cards with unique dish names and cook times for a cooking app." that directly supports the article use case. The output should be specific, reviewable, and reproducible rather than a generic demonstration. Keep the requested goal as the primary outcome and avoid adding unrelated features, assumptions, or unsupported capabilities.

Expected output:
The final result should directly satisfy the "Recipes Replace recipe cards with unique dish names and cook times for a cooking app." requirement and be understandable without guessing what was supplied or configured. A successful run should produce the intended output in the requested format, with the important values and workflow decisions clear enough to reproduce the result. If the workflow can fail, the expected behavior should also make the failure visible and provide a clear next action instead of silently producing an incomplete result.

Scenario:
A realistic figma-ai workflow is being prepared for Use Figma AI for First Draft Explorations using figma-ai. The starting requirement is: Travel Replace destination cards with unique city names and trip lengths. No airline logos in text.. The work should be tested on a representative input first so the result can be reviewed before the same process is applied to the full project or production workload.

Objective:
Create a production-ready result for "Travel Replace destination cards with unique city names and trip lengths. No airline logos in text." that directly supports the article use case. The output should be specific, reviewable, and reproducible rather than a generic demonstration. Keep the requested goal as the primary outcome and avoid adding unrelated features, assumptions, or unsupported capabilities.

Expected output:
The final result should directly satisfy the "Travel Replace destination cards with unique city names and trip lengths. No airline logos in text." requirement and be understandable without guessing what was supplied or configured. A successful run should produce the intended output in the requested format, with the important values and workflow decisions clear enough to reproduce the result. If the workflow can fail, the expected behavior should also make the failure visible and provide a clear next action instead of silently producing an incomplete result.

Scenario:
A realistic figma-ai workflow is being prepared for Use Figma AI for First Draft Explorations using figma-ai. The starting requirement is: HR portal Replace policy list items with unique policy titles. Keep Confidential tags where present.. The work should be tested on a representative input first so the result can be reviewed before the same process is applied to the full project or production workload.

Objective:
Create a production-ready result for "HR portal Replace policy list items with unique policy titles. Keep Confidential tags where present." that directly supports the article use case. The output should be specific, reviewable, and reproducible rather than a generic demonstration. Keep the requested goal as the primary outcome and avoid adding unrelated features, assumptions, or unsupported capabilities.

Expected output:
The final result should directly satisfy the "HR portal Replace policy list items with unique policy titles. Keep Confidential tags where present." requirement and be understandable without guessing what was supplied or configured. A successful run should produce the intended output in the requested format, with the important values and workflow decisions clear enough to reproduce the result. If the workflow can fail, the expected behavior should also make the failure visible and provide a clear next action instead of silently producing an incomplete result.

Scenario:
A realistic figma-ai workflow is being prepared for Use Figma AI for First Draft Explorations using figma-ai. The starting requirement is: Marketplace Replace seller cards with unique shop names and categories. Clearly sample data.. The work should be tested on a representative input first so the result can be reviewed before the same process is applied to the full project or production workload.

Objective:
Create a production-ready result for "Marketplace Replace seller cards with unique shop names and categories. Clearly sample data." that directly supports the article use case. The output should be specific, reviewable, and reproducible rather than a generic demonstration. Keep the requested goal as the primary outcome and avoid adding unrelated features, assumptions, or unsupported capabilities.

Expected output:
The final result should directly satisfy the "Marketplace Replace seller cards with unique shop names and categories. Clearly sample data." requirement and be understandable without guessing what was supplied or configured. A successful run should produce the intended output in the requested format, with the important values and workflow decisions clear enough to reproduce the result. If the workflow can fail, the expected behavior should also make the failure visible and provide a clear next action instead of silently producing an incomplete result.

Scenario:
A realistic figma-ai workflow is being prepared for Use Figma AI for First Draft Explorations using figma-ai. The starting requirement is: Podcast Replace episode list with unique titles and durations for a productivity podcast app.. The work should be tested on a representative input first so the result can be reviewed before the same process is applied to the full project or production workload.

Objective:
Create a production-ready result for "Podcast Replace episode list with unique titles and durations for a productivity podcast app." that directly supports the article use case. The output should be specific, reviewable, and reproducible rather than a generic demonstration. Keep the requested goal as the primary outcome and avoid adding unrelated features, assumptions, or unsupported capabilities.

Expected output:
The final result should directly satisfy the "Podcast Replace episode list with unique titles and durations for a productivity podcast app." requirement and be understandable without guessing what was supplied or configured. A successful run should produce the intended output in the requested format, with the important values and workflow decisions clear enough to reproduce the result. If the workflow can fail, the expected behavior should also make the failure visible and provide a clear next action instead of silently producing an incomplete result.

Scenario:
A realistic figma-ai workflow is being prepared for Use Figma AI for First Draft Explorations using figma-ai. The starting requirement is: IoT Replace device cards with unique device names and online statuses for a smart home app.. The work should be tested on a representative input first so the result can be reviewed before the same process is applied to the full project or production workload.

Objective:
Create a production-ready result for "IoT Replace device cards with unique device names and online statuses for a smart home app." that directly supports the article use case. The output should be specific, reviewable, and reproducible rather than a generic demonstration. Keep the requested goal as the primary outcome and avoid adding unrelated features, assumptions, or unsupported capabilities.

Expected output:
The final result should directly satisfy the "IoT Replace device cards with unique device names and online statuses for a smart home app." requirement and be understandable without guessing what was supplied or configured. A successful run should produce the intended output in the requested format, with the important values and workflow decisions clear enough to reproduce the result. If the workflow can fail, the expected behavior should also make the failure visible and provide a clear next action instead of silently producing an incomplete result.

Scenario:
A realistic figma-ai workflow is being prepared for Use Figma AI for First Draft Explorations using figma-ai. The starting requirement is: Nonprofit Replace campaign cards with unique campaign titles and progress percentages. Sample only.. The work should be tested on a representative input first so the result can be reviewed before the same process is applied to the full project or production workload.

Objective:
Create a production-ready result for "Nonprofit Replace campaign cards with unique campaign titles and progress percentages. Sample only." that directly supports the article use case. The output should be specific, reviewable, and reproducible rather than a generic demonstration. Keep the requested goal as the primary outcome and avoid adding unrelated features, assumptions, or unsupported capabilities.

Expected output:
The final result should directly satisfy the "Nonprofit Replace campaign cards with unique campaign titles and progress percentages. Sample only." requirement and be understandable without guessing what was supplied or configured. A successful run should produce the intended output in the requested format, with the important values and workflow decisions clear enough to reproduce the result. If the workflow can fail, the expected behavior should also make the failure visible and provide a clear next action instead of silently producing an incomplete result.

Scenario:
A realistic figma-ai workflow is being prepared for Use Figma AI for First Draft Explorations using figma-ai. The starting requirement is: Agent bulk Ask the agent: populate selected cards with realistic inventory SKUs and stock counts. Keep layout.. The work should be tested on a representative input first so the result can be reviewed before the same process is applied to the full project or production workload.

Objective:
Create a production-ready result for "Agent bulk Ask the agent: populate selected cards with realistic inventory SKUs and stock counts. Keep layout." that directly supports the article use case. The output should be specific, reviewable, and reproducible rather than a generic demonstration. Keep the requested goal as the primary outcome and avoid adding unrelated features, assumptions, or unsupported capabilities.

Expected output:
The final result should directly satisfy the "Agent bulk Ask the agent: populate selected cards with realistic inventory SKUs and stock counts. Keep layout." requirement and be understandable without guessing what was supplied or configured. A successful run should produce the intended output in the requested format, with the important values and workflow decisions clear enough to reproduce the result. If the workflow can fail, the expected behavior should also make the failure visible and provide a clear next action instead of silently producing an incomplete result.

Tips

Exclude approved copy from selection
Name the product domain
Forbid real customer data
Manually fix impossible numbers
Label prototypes as sample data
Rename layers after large content passes

Verification notes

Replace content is listed in Use AI tools in Figma Design and Actions menu help as swapping placeholder or duplicate text with unique realistic content. When plan access, seat rules, or credit behavior differ from memory, open help.figma.com and your AI balance. Limits change, so do not promise client timelines from outdated screenshots.

Keep a short session log beside the file: tool used, selection, prompt, and the one change you will try next. That habit turns four variants or parallel agent threads into learning instead of noise.

When Actions are missing, check whether an admin disabled Figma AI for the workspace before rewriting prompts. Access problems look like prompt problems until you confirm settings.

Treat the first AI pass as a sketch. Compare against your spacing grid, type styles, and accessibility targets before inviting stakeholders.

If you collaborate, store winning prompts on a shared page next to the library link. Reuse the skeleton and change only product nouns or channel constraints.

Separate exploration files from production libraries. Explore freely in drafts, then rebuild keepers with real components before handoff.

Close each session by naming frames clearly and discarding dead ends. A clean page saves time when someone asks for the pricing riff from Tuesday.

Prefer concrete product language over vague vibes alone. Screen type, primary action, and one constraint outperform a pile of adjectives.

Confirm AI credit balance and reset date before large image or agent batches. Shared credits power many Figma AI surfaces.

Lock marketing headlines and legal footnotes before Replace content runs across a marketing page.

Invented metrics can look persuasive in a review. Label prototypes as sample data when numbers are not real.

Re run Replace content on stubborn cards only. Full page rewrites can scramble copy you already approved.

Credibility is the goal. Reviewers trust a prototype more when every card feels specific, even when numbers are labeled sample.

Domain briefs prevent random content. A fintech dashboard filled with recipe titles wastes a review cycle.

Exclude navigation chrome and footers unless you truly want them rewritten. Those layers are usually already intentional.

After Replace content, spot check sorting and alignment. Longer unique strings can wrap and reveal auto layout debt.

Never paste real customer exports into prompts to make content feel authentic. Use clearly fictional names and values.

If two cards still match after a pass, narrow the selection to those cards and run again instead of reprocessing the whole page.

Combine with Rename layers before developer handoff so content and structure both look intentional.

For localized prototypes, unique English first, then translate. Translating duplicate lorem multiplies nonsense.

Pair Replace content with a one line stakeholder note: sample data only. That sentence prevents awkward metric debates.

Use neutral industries when the product is stealth. Overly specific fake company names can leak narrative you did not intend.

Check right aligned numbers after unique values appear. Longer figures expose alignment bugs that identical 99 placeholders hid.

When cards include images and text, replace text first, then decide whether image fills still look repetitive.

Replace content is a credibility tool for critiques, user tests, and executive reviews. Empty lorem blocks make feedback abstract and less useful.

Write the domain brief as if you were briefing a junior writer: product type, forbidden data, tone, and examples of good versus bad values.

Watch for duplicated strings that sit outside cards, such as toast messages or tooltips. Expand selection carefully if those still look fake.

After unique content lands, run a quick accessibility pass on truncation. Longer names can clip inside fixed chips and avatars.

Do not use Replace content to manufacture fake testimonials that imply real customers. Fiction belongs in clearly labeled concept work.

Keep a clean duplicate page before large replacements when the file is shared live. Instant collaboration means someone may review mid edit.

Schedule Replace content just before critique, not weeks early. Early unique content goes stale when the product story changes.

If values must stay blank for privacy demos, say so in the brief so AI does not invent realistic looking secrets.

Unique content also helps motion and prototype demos because repeated strings make every state feel identical even when the UI changed.

Reject outputs that invent currencies, medical dosages, or legal outcomes. Those categories need human authored sample rules.

Finish by scanning for leftover identical titles across the page. One missed duplicate card undermines the realism of the whole review.

Common mistakes

Running Replace content on design system docs
Presenting invented metrics as factual
Overwriting locked marketing headlines
Using real employee or customer names
Skipping a second pass on leftover duplicates
Localizing before content is unique

Related Figma AI articles: /blog/how-to-use-figma-ai-to-rewrite-translate-and-shorten-text, /blog/how-to-use-figma-ai-for-prototype-interactions-and-layer-hygiene, and /blog/how-to-use-figma-ai-for-first-draft-explorations. Return to /explore/figma-ai for the broader guide set.