The problem
The goal was not to generate more articles. It was to produce football education that remained factual, useful, consistent and worth publishing.
Internal R&D Case Study · Production Candidate
Quality-Gated Knowledge Publishing System
A knowledge and educational content platform where AI assists with structure and explanation, while facts, terminology, depth and publication remain controlled by rules and human review.
01Data
02Draft
03Fact check
04Depth gate
05Human review
06Publish
How the problem was understood
Hard facts, teaching depth and editorial voice were separated because each needs a different kind of control.
Design approach and solution
Trusted data governs scores, names and dates. AI helps organise and explain. Rules assess teaching depth, and a human remains responsible for recommendation and publication.
Where AI was deliberately restricted
AI cannot decide that an uncertain fact is true or force shallow material into the published layer. Content can be refused.
Safeguards and reliability
The pipeline moves from data to draft, fact check, depth gate, human review and publication. Fact, teaching and voice remain distinct layers.
Current status and reusable lessons
This is a personal R&D project and production candidate, not a claim of full launch or external client work. Its reusable lesson is that quality systems need the right to stop, not only the ability to generate.
Technical notes, in plain English
Three responsibilities, deliberately separated
FACT — what can be verifiedTEACHING — what helps a reader learnVOICE — how the explanation feels
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