David Ekong

David EkongOct 15, 2026

“This was written by AI” gets treated like a red flag. It shouldn't be — on its own. The real question is never whether AI wrote something. It's whether a human with standards reviewed it before it went out. I say this as someone whose actual job is reviewing AI output all day. The models are good. Genuinely good. But good doesn't mean reliable, and reliable is the thing that matters when the output is going in front of a client, a customer, or a decision-maker. What I see most often isn't bad AI output — it's AI output that skipped the review step entirely. Teams treat the first draft as the final draft because it sounds confident. Confidence isn't the same as correctness. That gap is exactly where my work lives — and it's the gap most businesses using AI tools haven't accounted for yet. Curious where others land on this — would you rather know content was AI-assisted, or not know at all as long as it's accurate?

David EkongOct 5, 2026

Everyone talks about “training data quality” like it's one thing. It isn't. In my day-to-day labeling and ranking AI/LLM outputs, “quality” usually breaks down into three separate questions: 1. Is it factually correct? 2. Is it actually useful to the person who asked? 3. Does it follow the specific guidelines for this task? A response can pass one of these and completely fail the other two. The most common mistake I see (and catch) is confidently wrong — text that reads well, sounds authoritative, and is just... incorrect. Those are the hardest errors to flag because nothing about the tone signals “check this.” This is 90% of what annotation work actually is. Not clicking “good” or “bad” — making a judgment call on which of these three things failed, and why. If you're building or buying AI training data, this is the question to ask your evaluators: not “did you check it,” but “which of the three did you check it against.” What's a piece of confidently-wrong content you've caught recently — AI-written or otherwise?

David EkongSep 30, 2026

I spent the last year training AI models to think more carefully — and it taught me something I didn't expect: most bad AI output isn't a "smart model, dumb mistake" problem. It's a lack of human precision somewhere upstream. Hi, I'm David 👋 I'm an AI Trainer and Data Annotation Specialist. Day to day, I label and rank LLM outputs, write and refine prompts, and fact-check AI-generated content against detailed guidelines — the unglamorous, detail-heavy work that decides whether a model actually gets better or just gets more confident. My background is science, not tech. I have a B.Sc. in Biochemistry, and before I ever labeled a data point, I was trained to question evidence, document rigorously, and never assume something is correct just because it looks right. That habit turned out to be exactly what AI quality work demands. Alongside that, I write and design — so I don't just catch when text is factually wrong, I catch when it reads badly, and I can help teams put together the visual assets to go with it. I'm here to: → Share what I'm learning about AI training, data quality, and RLHF from the inside → Connect with AI teams, founders, and businesses who need someone detail-obsessed on their side → Be useful, consistently, before I ever ask for anything If you're working on an AI project, building content, or need a second pair of eyes that won't miss the small stuff — let's connect. I'd love to hear what you're building. #AITraining #DataAnnotation #RLHF #FreelanceWriter #OpenToWork