Original Reddit post

I built an iOS recipe app called Prompt en Place, so take my perspective accordingly. I want to talk about the technical realities of putting AI into a cooking context — what’s genuinely hard, what works, etc. (Clearly, there is some self-promo angle here, so full disclosure, link below. I hope this is interesting regardless.) RECIPE EXTRACTION IS MESSIER THAN IT SOUNDS The app imports recipes from URLs. This sounds simple. It is not. Recipe websites have wildly inconsistent HTML structures. Some use schema.org markup, which is great when it’s there and often incomplete when it is. Many bury the actual recipe under 2000 words of life-story prose and ad blocks. Some render the recipe content entirely in JavaScript so it’s not in the initial HTML at all. I ended up with a pipeline that tries structured data extraction first (no AI at all, this is open source based called recipe-scrapers), falling back to some custom HTML parsing for Instagram, TikTok, and YouTube using an LLM as the final cleanup layer when everything else produces garbage. Pushing a whole rendered-out HTML page into an LLM eats your token budget for breakfast otherwise. Lesson Learned: Don’t blindly throw stuff into an LLM, especially not HTML. This will get expensive, very quickly. Camera import from physical cookbooks is a separate problem. OCR gives you raw text, but cookbook layouts are unpredictable — multi-column formats, ingredients that wrap oddly, steps that reference sub-recipes on other pages, abbreviations that assume you already know what “mod. oven” means. The LLM has to reconstruct logical structure from what is often a mess. This, on the other hand, is super simple. One image is just a few tokens, the system prompt here makes the difference to get everything out in the form that you want. (And structured decoding, which is a feature that eg. Gemini supports to avoid unpredictable JSON mess.) Lesson Learned: Use structured decoding when you want a specific technical output format such as JSON. Don’t tell the AI to output JSON and accept plain text. SMART SCALING ISN’T MULTIPLICATION Every recipe app scales by multiplying ingredient quantities. But cooking doesn’t work that way. Double a cake recipe and you don’t double the baking time — you need a bigger pan and slightly longer baking at a slightly lower temperature because the center takes longer to set. Halve a stew and the liquid-to-surface-area ratio in your pot changes, so reduction happens faster. Salt and spices don’t scale linearly at all. Getting AI to reason about these physical relationships instead of just doing arithmetic was genuinely interesting work. It uses the LLM to think about why scaling changes technique, not just quantities. It’s not perfect — I’d trust a pastry chef over any model for baking ratios — but for weeknight cooking it’s a real improvement over linear multiplication. This is something that is genuinely super useful and I have never seen it in any other app. Lesson Learned: Sometimes you are still in awe when you see something like this come to life although in hindsight it does not seem like a very complex task for an LLM. AI IMAGE GENERATION FROM RECIPE TEXT A recipe contains no visual description whatsoever. When you ask an image model to generate a photo of a dish from just a title, ingredient list, and instructions, it has to imagine everything visual: the plating, the vessel, the garnish, the lighting, the color of the sauce. It has to know that a beef bourguignon should look different from a stir-fry based purely on understanding what those dishes are. Results are often surprisingly good. And sometimes hilariously wrong. Gemini, for instance, seems to really think all desserts need to have a sprig of mint as garnish whether or not mint appears anywhere in the recipe. Curries get a scattering of chili flakes on top regardless of the actual spice profile. It has internalized food photography conventions — what dishes are “supposed to” look like in a magazine — rather than reasoning about what the specific recipe would actually produce. If you can live with a few hallucinations, images still look super stunning. (I am using Nano Banana.) Lesson Learned: Nano Banana (and probably other modern image gen models) don’t necessarily need the “This is what I want to see” description; they can “imagine” how a finished product looks like from a set of instructions to produce it. Very cool. THE “ELEVATE THIS DISH” FEATURE The most interesting AI application in the app. You take a home recipe and ask “how would a professional cook approach this?” The responses are grounded in real technique: toast your spices before grinding them, bloom tomato paste in oil before adding liquid, rest meat at room temperature before searing, deglaze the fond. These aren’t flavor additions — they’re process improvements that come from the model having absorbed a huge corpus of culinary knowledge. This is where AI genuinely adds value in a cooking context. Lesson Learned: (Maybe the least interesting lesson) – AI is generally good at cooking. At least Gemini. WHAT STILL NEEDS HUMAN JUDGMENT I used AI heavily to build this app itself (Claude Code for development). Code generation is fast and functional. But every UX decision, every screen flow, every moment where the question is “how should this feel to use” — that stayed entirely human. I often wrote a multi-page UX description before letting the AI implement anything. The tool will build exactly what you describe. It will not tell you that what you described is confusing, that a user will get lost three screens in, or that you’re putting too much on one page. You can also get away with describing just a feature and Claude will give you “some result”, but I learned that if you have no idea how the user flow should be, error scenarios, etc., you only get a half-baked version (pun intended, sorry). I still managed to build the whole thing in about 3 weeks (not full time). This would have been impossible without heavy use of Claude Code. Lesson Learned: AI is strong at specific, well-defined tasks: parse this HTML into a recipe, reason about how scaling affects cooking time, suggest technique improvements. The holistic question of whether an app is actually good to use – the core UX, to me seems still unsolved by AI alone. (Maybe this is a good thing?) ONE MORE THING Now the self-promo part. If you want to check it out: App Store: https://apps.apple.com/app/prompt-en-place/id6760935094 PS: I wrote this post myself. Just in case anyone wonders, just proofreading and formatting was AI. ;) submitted by /u/scurr4

Originally posted by u/scurr4 on r/ArtificialInteligence