Quality Rules That Keep It From Looking Like AI | DATASPHERES AI - Dataspheres AI

Overview Viewers can tell when content was rushed out by a machine. So can platforms. YouTube, for example, will not pay for content that is "mass-produced...

Overview Viewers can tell when content was rushed out by a machine. So can platforms. YouTube, for example, will not pay for content that is "mass-produced or repetitive" ( YouTube’s channel monetization policies ). Dataspheres runs every show through 22 written quality rules. Some are checked by machine and block the next step. Others are a checklist you or a reviewer must pass. Here they are in plain words, grouped by what they protect. Lesson 3.1: Show real people Real faces. Whenever the show talks about a person, it shows that person's verified official headshot. If a real photo does not exist, it may use a clearly labelled AI image of a role or scene, but never a fake photo standing in for a real, named person. Lots of logos and headshots. Use as many verified team logos and player headshots as the data supports. Real hosts. The show uses your datasphere's designed AI hosts, each with a realistic headshot and a matched voice. Never a random fallback cast. Lesson 3.2: Every claim has a reason and a source Every pick says why. A pick needs data: injury status, the Vegas spread and team total, the team's net points per game (scored minus allowed), and a key matchup stat. A pick without a reason is refused. Follow every name through. If the show mentions a player, it states their status and makes a clear call. A name mentioned once and dropped is refused. Sourced, dated numbers. Every fact and salary comes from this week, with its source. Salaries from only one source are flagged. FanDuel prices are never used as DraftKings prices. Lineups are checked by script. Every lineup is tested against the salary cap and roster rules before it appears. Research is cited. The deck, kit and article link the research and list the dated sources. Lesson 3.3: Know the audience Research the audience's goal first. For DFS, that means the contest, the field size, what wins, which players everyone is picking and where the edge is. Build a story arc on that goal. Hook, stakes, where the edge is, the plays, the lineups, why each can win, then the call to action. Deliver the main promise. For a DFS show, that means three tiered lineups (safe, mid, high risk), plus top, mid and high-risk picks at every position. Every cut of the video covers all three lineups. Lesson 3.4: Look like a broadcast, not a document Broadcast graphics. Player cards, matchup splits, charts that build, headshot walls, large type, no big empty zones. Every slide should work as a thumbnail. Tables only when nothing else fits. Motion with purpose. The camera never zooms into a table. Cards slide in, charts build, numbers count up. Readable captions inside the frame, clear of each app's buttons on vertical video. No repeated jokes. No running bit or stock line repeats from an earlier episode. Audiences notice. The owner noticed a repeated "spreadsheet" joke and made it a rule. Lesson 3.5: Review before anyone else sees it Watch it yourself , the whole video plus the script, before it goes to the approver. Iterate at least twice. The rubric: a hook in 3 seconds, a reason to keep watching, clear stakes, concrete numbers, one idea per slide, a change of pace every 6 to 10 seconds, a payoff and a call to action. Recorded approval for every stage, and every run goes through the content pipeline agent, which applies these checks. Lesson 3.6: Links, images and calls to action Live links only. Every link and QR code points to https://dataspheres.ai, never a test address. It must open a real, public page. A dead link blocks the publish. Feature image = thumbnail. Every published piece has a feature image. The 1280x720 YouTube thumbnail (big faces, a huge hook number or word, team colours, readable on a phone) is also the article banner and the share card. Two kinds of call to action. One or two context CTAs for the episode (follow this datasphere, join its newsletter, take a survey, read the article), each with a QR code. And every post ends with the course CTA. A CTA whose target is missing is dropped, never printed. Every artifact comes with its title and caption , in kit order. Two outputs every time: the presentation and the narrative article. Do this now Take the show from Module 2. Go through every lesson above as a checklist. Write down each rule it breaks. Ask your AI tool to fix them, then re-approve the stages you changed. Quiz What must every pick include? a) An emoji b) A data reason: status, Vegas line and team total, net points per game, a matchup stat c) A joke Answer: b Where must every link and QR code point? a) A real, public page on the live site b) Any address that loads c) A test server Answer: a What is wrong with reusing a joke from last week? a) Nothing b) Viewers notice repetition, and it makes the show feel machine-made c) It is too short Answer: b How the Platform Modules Work Modules 4 to 11 cover one platform each. They share four ideas: Native first, link second. Upload the file to the platform itself. Put links in the first comment or r