{"slug": "i-had-5-ai-features-that-worked-individually-the-product-still-felt-broken", "title": "I Had 5 AI Features That Worked Individually. The Product Still Felt Broken", "summary": "A developer building the tattoo-design product AIMakeTattoo found that five individually working AI features — concept generation, image-to-tattoo, lettering, refinement and try-on — still produced a broken-feeling product because outputs could not flow between steps. The fix was not another model call but treating each generated result as a shared object moving through a single workflow, from idea through generate, modify, try-on, compare and artist handoff. The developer argues that a successful API response does not mean a user completed a task, and that the handoff between tools is part of the product itself.", "body_md": "I had five AI features that all worked on their own.\n\nNone of them was broken.\n\nThe product still felt broken.\n\nThat was the part I didn’t expect.\n\nI had spent most of my time thinking about each feature as its own problem:\n\nCan the generator produce a useful concept?\n\nCan image-to-tattoo transform an uploaded image?\n\nCan lettering preserve exact text?\n\nCan refinement change the result without starting over?\n\nCan try-on place the design on a body photo?\n\nIndividually, the answer was mostly yes.\n\nBut that didn’t automatically make the product feel coherent.\n\nThe problem was what happened **between** them.\n\nA user could generate something they liked, but then the next step wasn’t always obvious.\n\nThey might want to:\n\nEach feature could do its own job.\n\nBut the user still had to mentally stitch the product together.\n\nThat was the real bug.\n\nThis was the point where I stopped thinking in terms of “what AI feature should I add next?”\n\nInstead, I started looking at outputs.\n\nWhat does each feature produce?\n\nAnd where should that output go next?\n\nFor my product, the useful path gradually became:\n\n**idea → generate → modify → try on → compare → artist handoff**\n\nThat flow sounds obvious when written down.\n\nIt wasn’t obvious in the product.\n\nThe useful change was not adding another model call.\n\nIt was making one result usable by the next step.\n\nThis was the mental model that helped most.\n\nInstead of thinking about Generator, Lettering, Image-to-Tattoo, Refinement, and Try-On as five separate tools, I started treating the generated result as a shared object moving through the product.\n\nA generation could become the input to refinement.\n\nA refined result could move into try-on.\n\nA lettering result could be previewed on the body.\n\nA saved result could be reopened instead of recreated.\n\nThat made the product feel less like a collection of demos and more like one workflow.\n\nI ran into this while building [AIMakeTattoo](https://aimaketattoo.com), where the original idea was much simpler: generate tattoo concepts.\n\nThe more features I added, the more obvious it became that generation itself was only one step.\n\nThe model work mattered.\n\nBut most of the frustrating product problems were outside the model:\n\nThose are ordinary product and software problems.\n\nAI just made it easier to hide them for a while because each isolated feature looked impressive on its own.\n\nThat’s probably the main thing I took away from this.\n\nA successful API response doesn’t mean the user successfully completed a task.\n\nA feature can pass every technical test and still create friction because its output has nowhere useful to go.\n\nBefore adding another feature now, I try to ask:\n\nThat checklist has been more useful than asking whether another AI capability would look impressive in the product.\n\nI used to think of the handoff between tools as implementation detail.\n\nNow I think it is part of the product itself.\n\nThe transition from **generate → modify** or **modify → try on** can matter as much as either feature on its own.\n\nThat changed how I think about building AI products.\n\nThe hard part is often not getting one more model to work.\n\nIt’s making sure the thing it produces can actually continue somewhere useful.\n\nHave you built a product where every feature worked individually, but the workflow between them was the real problem?", "url": "https://wpnews.pro/news/i-had-5-ai-features-that-worked-individually-the-product-still-felt-broken", "canonical_source": "https://dev.to/warrenshi/i-had-5-ai-features-that-worked-individually-the-product-still-felt-broken-7fe", "published_at": "2026-10-18 04:01:43+00:00", "updated_at": "2026-10-18 04:09:58.652219+00:00", "lang": "en", "topics": ["ai-products", "generative-ai", "ai-tools"], "entities": ["AIMakeTattoo"], "also_reported_by": [], "alternates": {"html": "https://wpnews.pro/news/i-had-5-ai-features-that-worked-individually-the-product-still-felt-broken", "markdown": "https://wpnews.pro/news/i-had-5-ai-features-that-worked-individually-the-product-still-felt-broken.md", "text": "https://wpnews.pro/news/i-had-5-ai-features-that-worked-individually-the-product-still-felt-broken.txt", "jsonld": "https://wpnews.pro/news/i-had-5-ai-features-that-worked-individually-the-product-still-felt-broken.jsonld"}}