Most bad AI portraits don’t come from the model. They come from vague prompts. After generating a lot of images using newer tools like Image GPT-2 and Google Nano Banana Pro, one thing became very clear, the difference between an average image and a really good one is usually just how you describe it. There’s actually some data around this too. In a lot of prompt testing communities, people have seen that just making prompts more specific can improve results by almost 40–50%, without changing any settings. 1. Start with real detail Instead of writing something like “beautiful woman smiling,” try describing what you actually want to see. “slight smile, eyes looking at the camera” That one small change already gives the model direction. Then layer in realism: - natural skin texture - visible pores - small imperfections This is what removes that overly smooth, plastic look. 2. Control the lighting (this matters more than you think) Lighting alone can completely change the result. Pick one clear style: - soft diffused lighting → clean, natural - window light from the side → adds depth - dramatic side lighting → more cinematic Mixing multiple lighting styles usually confuses the model, and that’s when things start looking off. 3. Push it toward photography AI tends to lean a bit “illustration-like” unless you guide it. Adding small cues helps a lot: - photorealistic - shallow depth of field - film grain - DSLR / mirrorless camera People have noticed that adding camera-related terms can make outputs feel way more consistent and real. 4. Use negative prompts This is something most people skip. But telling the model what you don’t want helps clean up a lot of issues: - deformed eyes or pupils - cartoon / anime / CGI look - duplicate faces - weird distortions Even simple negative prompts can reduce visible errors quite a bit. 5. Be specific with textures This is where realism actually comes from. Instead of generic words: - Skin → pores, fine texture, slight variation

I joined this community on Christmas Day of all days, and I've been here just over 4 months now. Since discovering vibe coding and really starting to dive into it in January, February, and March ,I have watched every single video produced by @Nate Herk @Nate Herk on YouTube. The thing about being a new AI builder is that it's so easy to get stuck in research mode, and that is where I was at *for months.* The dream I had to create something useful and real, was getting mired in thinking I needed to learn more and more before trying to launch something. Everything in Nate's videos started to click once I really started to dig into the tools, make mistakes, try to fix them, work around it, and persist through the disappointment when my first several tries didn't come out the way I wanted. Last night I shipped my first Progressive Web Application and I have Nate to thank. It supports the members of my Skool community by helping them come up with a structured plan for goal attainment. (Funny story: I used the prompt based version of that plan to actually build the PWA during the month of April.... that's so meta) 🤣 Tonight I am working on what will end up becoming my first real production app. It's being entered in a contest on Friday. Fingers crossed. The HUGE unlock for this big app deployment was Nate's video he did on using the Claude skill /superpowers I have been in a planning session with the /superpowers skill for about two hours refining everything on a local host screen until the UI/UX is just right. It then built an awesome deployment handbook for me and saved it in the local documents folder. How cool is that. The goal of using the /superpowers skill is that it takes 95% of the upfront time to nail down all the details and get them right so that when you use massive amount of tokens to make your build, you aren't disappointed with the output. I know that one-shot builds are pretty rare and I'm not expecting perfection, but this should get me 90% of the way there.