CineMind uses AI as a research and drafting assistant — never as an unsupervised publisher. This page explains exactly how AI is used, where humans stay in the loop, and how we guard against the failure modes AI systems are known for.
What AI does
- Ingestion. AI monitors ~40 free RSS sources across science, technology, health, business, and culture. Candidate topics are queued for review.
- Drafting. Approved topics are drafted by large language models (Google Gemini and OpenAI) against structured prompts that enforce sourcing, section structure, and reading level.
- Enrichment. Hero images are sourced from Wikipedia and Openverse with attribution preserved. Timelines, FAQs, and glossary terms are extracted from cited sources.
- Evolution. Every article is re-passed at 24h, 7d, 30d, and annual cadences to incorporate fresh data.
What AI does NOT do
- Publish without a version stamp and author assignment.
- Cite sources that do not exist. (All citations are validated against the ingested source list.)
- Make medical, legal, or financial recommendations. Educational content only.
- Impersonate real individuals, dead or living, in first person.
Human oversight
- New topics enter a moderation queue; editors approve, reject, or edit before publishing.
- Every published article has a named author profile.
- Reader corrections trigger human review and a public changelog entry.
- Automated evolution jobs are rate-limited (max 5 articles/day/brand) and logged in an admin audit trail.
Models used
- Google Gemini (drafting, evolution, translation).
- OpenAI GPT (text-to-speech, karaoke sync).
- Wikipedia REST API (facts, hero images).
- Openverse (CC-licensed media).
Data used to train models
We do not train third-party AI models on user data. Reader interactions (chats, likes, bookmarks) stay inside our platform and are used only to improve our own ranking and recommendation logic.
Related
Exactly where machine assistance sits in our workflow
Selection
Subjects are chosen by a human, from recurring questions in sessions, gaps between existing essays, and terms the reference layer uses but has not yet defined. A model may cluster those signals; it does not decide what deserves an essay.
Each commission is scoped by hand before drafting: what it will argue, which viewer it is for, and what evidence would defeat it.
Research and drafting
Research is AI-assisted against public sources — interviews, production records, published criticism — with every factual claim traced to something linkable before it survives the draft. Unsupported assertions are cut rather than hedged.
First drafts are machine-generated from the human brief and treated as raw material. Structure often survives; specific claims about what is on screen rarely do without verification.
Watching and checking
Claims about shots, cuts, staging and sound are checked against the film itself. Counts are counted. This is the slowest part of the process and the reason the publication schedule is modest.
Where a detail cannot be verified, the essay either drops it or attributes it to the source that asserted it. It is never smoothed into the prose as though we had seen it.
Images and embedded media
Cover images are licensed photography selected per piece, so a listing page does not repeat one illustration across a dozen essays. We do not pass synthetic images off as production stills.
Embedded reference videos are third-party material chosen for relevance and labelled as external — supporting evidence, never a replacement for the argument in the text.
After publication
Essays are revisited when related material changes or a reader reports a problem, and revisions carry a version indicator so an update is distinguishable from a reprint.
Pieces that no longer meet the current standard are rewritten or withdrawn rather than left in the index to pad a count.