Build a Dining App in 7 Days with LLMs: Step‑by‑Step Rapid Prototyping Tutorial

Cut decision fatigue — ship a dining app in a week using ChatGPT & Claude

If you’re a developer or IT lead tired of long spec cycles, stakeholder drift and over‑engineered MVPs, this tutorial gives you a practical, repeatable 7‑day blueprint to deliver a usable dining app that leverages LLM integration (ChatGPT / Claude), simple backend APIs, a compact data model, and fast UX iteration. You’ll get concrete daily deliverables, example prompts, architecture patterns (RAG, embeddings, function calling), and publishing tips so you can prototype, test and publish quickly in 2026’s AI‑driven tooling landscape.

Why a 7‑day dining app is realistic in 2026

Micro apps — short‑lived, single‑purpose apps built by small teams or solo makers — are mainstream. Advances in model APIs, long contexts, reliable function calling, and developer tools like Anthropic’s desktop Cowork preview (Jan 2026) and improved OpenAI/third‑party SDKs let makers “vibe‑code” a working product fast. Rebecca Yu’s week‑long Where2Eat build is a concrete example of how non‑specialists can iterate quickly with LLMs and off‑the‑shelf cloud services.

“Once vibe‑coding apps emerged, I started hearing about people with no tech backgrounds successfully building their own apps,” — Rebecca Yu, creator of Where2Eat.

High‑level plan (inverted pyramid): what you deliver in 7 days

1. Day 1: Project scope, stack, and minimal data model.
2. Day 2: Backend API + LLM integration prototype (ChatGPT or Claude).
3. Day 3: Frontend skeleton (web or Expo mobile) + basic flows.
4. Day 4: Recommendation logic: prompts, embeddings & RAG.
5. Day 5: Group voting UX, session state, and analytics hooks.
6. Day 6: Testing, privacy/security review, and polish.
7. Day 7: Publish to TestFlight / internal Play Store / Vercel + launch checklist.

Tech choices for rapid prototyping (what to pick and why)

• Frontend: Next.js (web) or Expo + React Native for mobile — both support fast iteration and OTA updates.
• Backend: Node/Express or Fastify serverless on Vercel/AWS Lambda — keep logic server‑side to protect API keys.
• LLM APIs: OpenAI Chat Completions / function calling or Anthropic Claude (choose one or offer both via feature flag).
• Embeddings / Vector DB: Pinecone, Weaviate or an open Milvus cluster for fast RAG on menus/reviews — pair this with strong MLOps and feature store practices for consistent retrieval.
• Database: PostgreSQL (Supabase) or Firebase/Firestore for rapid auth + persistence.
• Telemetry: PostHog or Firebase Analytics for retention metrics.

Day 1 — Define scope, UX flows and the minimal data model

Goal: ship a working group decision flow with recommendations and voting.

Deliverables

• One‑page spec: group creation → choose vibe → LLM recommendations → vote → final pick.
• Data model (JSON schema) for MVP.

Minimal data model (JSON sketch)

{
  "User": {"id":"uuid","name":"string","preferences":{"diet":"string","cuisines":["string"]}},
  "Group": {"id":"uuid","name":"string","members":["userId"],"location":{"lat":num,"lng":num}},
  "Session": {"id":"uuid","groupId":"uuid","vibe":"casual|date|budget","constraints":{},"candidates":["restaurantId"],"votes":[]},
  "Restaurant": {"id":"string","name":"string","coords":{"lat":num,"lng":num},"priceLevel":1,"cuisine":"string","score":num}
}

Advice: keep fields explicit (no giant freeform user blobs) so you can easily map them to function outputs from the LLM.

Day 2 — Backend API and first LLM call

Goal: a secure server endpoint that calls an LLM and returns a structured recommendation list.

Architecture

• Server‑side endpoint: /api/recommend — accepts {groupId, constraints} and returns candidate restaurants + rationale.
• Store API keys only on the server. Never call LLMs directly from the client.

Function‑calling pattern (recommended)

Use the LLMs’ function calling (or structured response) feature to get consistent JSON that maps to your data model. Example pseudocode (generic):

// Server pseudocode
const prompt = `System: You are a dining assistant. Output JSON with field 'candidates' (array).
User: Group prefs: ${JSON.stringify(prefs)}. Nearby restaurants: ${JSON.stringify(rests)}.`

// Call LLM with function/schema for get_recommendations
const response = await LLM.chat.complete({messages:[...], functions:[{name:'get_recommendations',parameters: {type:'object',properties:{...}}}]})
const recommendations = response.function_call.arguments

Why this works: function calling enforces structure so your frontend can render without complex parsing.

Day 3 — Frontend shell and basic flows

Goal: build the core UI: group creation, vibe chooser, recommendation list, voting screen.

UX priorities for fast iteration

• Mobile‑first: 80% of use will be on phones—keep controls large and tap targets obvious.
• Microcopy: Use short prompts to instruct the LLM (e.g., "Filter to nearby, highly rated casual spots").
• Skeleton states: show loading skeletons while LLM waits (LLMs can be slow; perceived speed matters).

Deliverables

• Group creation screen with invite link (short‑lived tokens) — send via SMS/DM.
• Vibe selector (tags) that maps to constraints: price/diet/distance.
• Recommendation list that displays LLM rationale under each card.

Day 4 — Add embeddings and RAG for context‑aware recommendations

Goal: improve relevancy by combining LLM reasoning with local context (menus, reviews, past group choices) using embeddings.

Why RAG matters for dining

LLMs are good at reasoning but limited in recall for local, dynamic info (menus, promotions, new openings). Embeddings plus a vector DB lets you fetch relevant documents (menu items, recent reviews) and feed them as context to the LLM.

Pipeline

1. Index documents: menu snippets, last 30 reviews, neighborhood guides — generate embeddings with the same model family you use for retrieval. See practical storage patterns in storage workflows.
2. Store embeddings in vector DB (Pinecone/Weaviate/Milvus).
3. On recommendation: query vector DB for top‑k docs relevant to the group’s constraints and include them as context to the LLM call.

Prompt pattern (concise)

System: You are a local dining assistant. Use the context to recommend.
Context: [top 3 docs from embeddings]
User: Group prefs: ... constraints: ...
Return JSON with 'candidates' and a short 'rationale' for each.

Day 5 — Social interactions: voting, tie‑breakers and reservation links

Goal: create the group dynamics — cast votes, run tie‑breakers, optionally pass reservation links through OpenTable or API partners.

Voting UX

• Allow ranked or single‑pick voting depending on complexity.
• Show live updates via websockets (Pusher, Supabase Realtime) so members see votes in near real time.
• Use the LLM as a neutral tie‑breaker: ask for the final recommendation with rationale and confidence score.

Integrations

• Reservation APIs: OpenTable, Yelp Reservations or direct partner URLs — prefer deep links when full integrations are too heavy.
• Maps: Google Maps or alternatives for directions and ETA calculations — consider micro-map hub patterns for fast local mapping and edge locality.

Day 6 — Test, secure and comply

Goal: run the app through a rapid QA checklist, harden security and ensure privacy promises are clear.

Quick QA checklist

• Functional: flows for group creation, invite, vote, finalize.
• Edge cases: groups with 2 members, no restaurants found, far‑away constraints.
• Performance: LLM latency — show intermediate UI and limit waiting to 10–15s for best UX. Add observability around offline and latency-sensitive UIs (observability for mobile offline features).

Security & privacy (practical rules)

• Never send raw PII or full payment details to LLMs. Anonymize or hash sensitive fields before embeddings or prompting.
• Store API keys in server env vars and rotate regularly.
• Document data retention: short retention for conversational context (e.g., 30 days) unless the user opts in.
• Prepare a concise privacy policy that states what is sent to third‑party LLMs and why (transparency is essential for App Store reviews).

Day 7 — Publish fast: TestFlight, Play internal or web deploy

Goal: get your app into testers’ hands and prepare for public listing.

iOS TestFlight

• Register as an Apple developer (if not already). Upload build via Xcode/Transporter or EAS/Expo for managed workflows.
• Include a privacy policy and a transparent description of any LLM usage in your TestFlight notes.

Android internal testing

• Upload an internal track in Play Console — include a short internal testing checklist and known limitations.

Web deploy

• Deploy on Vercel/Netlify. Use serverless functions for the LLM endpoint and protect with API keys and rate limiting. See cost patterns and runtime choices in Kubernetes runtime trends and serverless cost governance.

Launch checklist

• Privacy policy and in‑app disclosure about LLMs (what is sent + retention).
• Basic analytics (session, retention, conversion to reservation).
• Monitoring for API cost and throttling (LLMs can be expensive at scale; set budgets & alerts).

Prompts, functions & examples you can copy

Here are compact, production‑ready prompt patterns and a sample function schema you can use to get reliable, typed responses from ChatGPT or Claude.

System + user prompt (template)

System: You are a concise dining assistant. Always return valid JSON matching the 'get_recommendations' schema. Prefer nearby options with at least 4★ or good recent reviews.
User: Context: {list of relevant docs}
User: Group preferences: {diet, priceRange, distanceKm}

Function schema (JSON schema example)

{
  "name": "get_recommendations",
  "parameters": {
    "type": "object",
    "properties": {
      "candidates": {"type":"array","items":{
        "type":"object",
        "properties":{
          "id":{"type":"string"},
          "name":{"type":"string"},
          "score":{"type":"number"},
          "rationale":{"type":"string"}
        },"required":["id","name","score"]
      }}
    }
  }
}

Tip: keep the schema narrow — the narrower the schema the fewer parsing errors in the client.

Monitoring, cost control and iterating after launch

LLM costs and latency are the two things that bite teams after launch. Use these practical guardrails:

• Cache LLM outputs per session for 24 hours to reduce repeat calls — and combine this with edge caching & cost control patterns for fast responses.
• Use a cheaper embedding / small LLM for retrieval and a stronger model only for final reasoning.
• Set hard rate limits and a cost budget. Send fallback static suggestions when budgets are exceeded.
• Track KPIs: time‑to‑final‑decision (TTFD), conversion to reservation, and retention after two weeks.

Advanced strategies and 2026 trends to leverage

Looking forward, integrate these capabilities to differentiate your dining app:

• Autonomous agents: Use agent tooling for multi‑step tasks like booking and follow‑up with the user. Anthropic’s Cowork and agent stacks now make lightweight automation accessible to non‑devs—good for automating repeated reservation flows.
• Multimodal input: Let users snap a menu photo and use OCR + LLM to extract dishes and map them to preferences.
• Edge LLMs: For offline suggestions or speed: run distilled models on‑device for cached recommendations; fall back to cloud LLMs for complex reasoning. See practical offline strategies in offline‑first edge node playbooks.
• Policy & trust: In 2026, regulators expect transparency for AI outputs. Provide a ‘Why this recommendation?’ toggle that shows the LLM’s evidence snippets.

Case study recap: Where2Eat lessons

Rebecca Yu shipped a simple, social web app in a week that focused on a single problem: group decision friction. She used LLMs for the heavy‑lifting of reasoning and kept the UI minimal. The lessons apply to any dev team:

• Start with a single clear use case (decide where to eat now).
• Rely on structured LLM outputs so the frontend is deterministic.
• Use off‑the‑shelf services (maps, reservation links) — don’t over‑integrate before validation.

Practical takeaways — your 7‑point checklist

1. Define a single MVP flow and data model on Day 1.
2. Keep LLM calls server‑side and use function calling for typed outputs.
3. Add embeddings + vector search by Day 4 to boost local relevance.
4. Use WebSockets for live voting feedback; keep latency UX friendly.
5. Anonymize PII before sending to LLMs; publish a clear privacy policy.
6. Cache LLM responses per session and set cost alarms.
7. Publish to TestFlight/Internal Play first, collect feedback, then iterate for public release.

Final notes & pitfalls to avoid

• Avoid sending full user chats to LLMs — prune context aggressively.
• Don’t rely purely on star ratings — merge them with recent review snippets via RAG.
• Expect model drift: periodically revalidate prompt outputs and update your safety filters.

Call to action

Ready to prototype? Start today: scaffold the backend with a single /api/recommend endpoint, wire function calling to your chosen LLM, and build the group UI in Expo or Next.js. If you want a starter repo with a Node server, sample prompts, and a Supabase schema tuned for dining apps, download our 7‑day starter kit and ship your MVP this week.

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