Original aggregate research
1,080 AI content production questions: research report
An aggregate analysis of 1,080 sanitized questions across AI image, video, UGC, product visual, prompt, workflow, and commercial production topics.
This is not a representative survey and it does not report search volume. It summarizes a sanitized planning bank built from transcript patterns, site gaps, search hypotheses, and forum-pattern observations.
Research snapshot
The corpus points to production decisions, not prompt collecting.
Tool/process, buyer-intent, troubleshooting, and workflow questions account for most classified intent. AI video production is the largest topic cluster in this internal bank.
Aggregate findings
Where the classified questions concentrate.
Counts show the composition of this dataset. They should guide coverage and answer design, but they should not be presented as market share or external demand.
What the mix suggests
Useful answers need to bridge generation and delivery.
Video questions carry the widest surface.
AI video production contributes 280 questions, with recurring workflow groups around motion prompting, video ads, and cinematic product videos. The editorial opportunity is diagnosis and planning, not another generic tool list.
Commercial intent is visible but must stay bounded.
Buyer-intent questions represent 195 records and conversion-stage questions represent 195. Answers should clarify scope, inputs, review gates, limitations, and next steps without promising performance or income.
Tool-specific coverage needs durable principles.
The bank includes 15 named tool or category groups with 40 questions each, plus 480 tool-neutral records. Public guidance should anchor changing interfaces to stable production decisions such as reference control, shot intent, QA, and handoff.
Question formats should match the job.
Detailed sections and checklists lead the recommended answer formats, followed by short answers, troubleshooting tables, and workflows. That mix argues for visible, task-shaped answers rather than repetitive FAQ blocks.
Methodology
How the aggregate was produced.
The public files can be inspected and cited. The underlying private corpus and sanitized question-level bank remain private.
- 01Measure the private corpus.
372 files were inventoried. 314 text-readable files were measured with a documented Unicode word-token rule, producing an estimate of 653,199 words.
- 02Normalize question patterns.
Private source material was used only to identify generalized questions, vocabulary, objections, and workflow gaps. Answers, quotes, timestamps, source titles, competitor frameworks, and full prompts were not carried into the public dataset.
- 03Classify the sanitized bank.
Each of the 1,080 original rewritten questions received source-signal, intent, funnel, topic, tool/category, workflow, answer-format, priority, and 1-5 editorial score labels.
- 04Publish aggregates only.
The committed JSON and CSV contain counts, shares, score averages, methodology metadata, and limitations. A reproducible local validator compares them with the private source bank without committing private rows.
sha256:9ccaf6b48d0575e795278ebab8a073f1c70bf21c69e58a23ec165251db3b64f4Open aggregate
Download the counts or cite the report.
The downloads contain no question text, transcript excerpts, source identities, private paths, or personal data.
AI Craft Academy. “1,080 AI content production questions: research report.” 2026-07-19. https://aicraft.academy/resources/ai-content-production-questions-report. Methodology version 1.0. The report reflects an internal research corpus and is not presented as a representative market survey.Limitations
What this report cannot prove.
- This is an internal corpus analysis, not a representative survey of creators, buyers, or the wider market.
- Counts describe the sanitized question bank and its editorial labels; they do not measure search volume, rankings, traffic, or product adoption.
- Transcript patterns, site gaps, search hypotheses, and forum patterns were normalized into original questions before aggregation.
- Tool behavior and audience vocabulary can change, so the report is a dated planning snapshot rather than a permanent benchmark.
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