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Micro Apps for Groups: Build a One-Day Dining App to Solve 'Where to Eat?' for Your Friends

UUnknown

2026-02-19

9 min read

End group-chat indecision: build a one-day micro-app using LLMs and low-code to recommend, vote, and order with friends.

Stop the group-chat paralysis: build a dining micro-app in a day

Everyone's hungry, nobody can decide. Group chats devolve into endless options, emoji fights, and finally someone says “whatever” — then orders fries alone. In 2026 you don’t need a dev team to fix that. With modern LLMs (Claude, ChatGPT), accessible low-code builders, and public restaurant data APIs, you can create a lightweight dining recommender — a micro app that helps your friends choose, vote, and order in under a day.

Why micro apps for group dining matter in 2026

Micro apps — small, single-purpose web or mobile apps made by individuals or small teams — exploded after 2024 and matured into 2025. By late 2025 we saw three things that make a one-day dining app realistic and valuable:

Smarter LLMs with larger context: Claude and ChatGPT models now handle long prompts and structured outputs, making recommendation logic implementable without heavy code.
Low-code + native AI integrations: Builders like Glide, Bubble, Softr, and a growing plugin ecosystem let you wire up LLM calls, maps, and databases visually.
Fresher, richer data: Menu and location APIs, plus improved RAG (retrieval-augmented generation) tooling, let your micro app include real-time menus and order links rather than vague suggestions.

“Vibe-coding” and fleeting personal apps have become mainstream — people build tools for friends and short-term needs, not enterprise deployments.

What this one-day dining micro-app will do (minimum viable features)

Ask a few quick questions: location, price level, cuisine, dietary restrictions, and time constraints.
Pull local restaurants and menus from APIs or a lightweight backend.
Use an LLM to score and create a short ranked shortlist with reasons.
Create a session link friends join to vote and comment.
Provide ordering paths: deep links to delivery, reservation links, or a phone number.
Tie-break and finalize with a quick algorithm (randomize, weighted vote, earliest arrive).

Tools you’ll want (2026-ready, low-code friendly)

Low-code builders: Glide (fast PWA), Bubble (UI control), Softr/Pory (Airtable-driven sites).
Backends: Airtable or Google Sheets for quick schemas; Supabase if you want a managed Postgres with auth.
LLM providers: Claude family (Anthropic) and OpenAI’s ChatGPT — both offer structured JSON outputs and tools for system prompts in 2026.
Automation & integrations: Zapier, Make.com, or n8n to connect APIs without code.
Data sources: Google Places / Places API, Yelp Fusion, Foursquare, and menu APIs (some restaurants publish menus via APIs or partnerships; otherwise use managed scrapers/APIs like Spoonacular or commercial suppliers).
Ordering partners: Deep links to Uber Eats, DoorDash, Grubhub or local providers; Stripe for payment splitting.
Optional: Vector DBs & RAG: Pinecone, Weaviate or managed RAG from your LLM provider, if you want faster context and menu search.

Step-by-step: Build your one-day dining micro-app

Estimated time: 4–8 hours if you follow this plan. No heavy dev work required; everything is low-code and uses LLM APIs.

Step 1 — Define the user flow (30–45 minutes)

Write the simplest flow that solves the problem: gather preferences → generate shortlist → create session → vote → finalize and order. Map the screens you need:

Landing: Join or create session + location permission.
Preferences: cuisine, price, diet tags, max distance, time to eat.
Shortlist: list of 3–6 recommendations with rationale and links.
Session: voting board + comment chat.
Result: chosen restaurant + ordering path and split-bill option.

Step 2 — Build a quick data backend (30–60 minutes)

Use Airtable or Google Sheets. Create tables/columns for:

Restaurants: id, name, address, lat, lon, price_level, cuisine_tags, menu_link, rating, ordering_links
Menus (optional): restaurant_id, item_name, price, dietary_tags
Sessions: session_id, host, location, preferences_json, selected_restaurants (ids)

Populate the restaurants table with a small sample (10–30 entries) for the test city using a Places or Yelp export, or paste key entries manually.

Step 3 — Wire the UI in a low-code builder (1–2 hours)

Pick Glide for the fastest path to a shareable PWA. Create pages based on the flow from Step 1 and connect them to your Airtable/Sheets data source. Key details:

Use a form component for preferences; store preferences JSON in the Sessions table.
Show the shortlist page as a dynamic list that will be filled by an LLM call (see Step 4).
Use a “Create session” button that generates a short link. Glide can generate shareable links and QR codes instantly.

Step 4 — Add an LLM-powered recommender (45–90 minutes)

We’ll keep it simple: call an LLM with a constrained prompt and a list of candidate restaurants (10–30 rows) from your sheet. The LLM will score and return a top 5 in structured JSON.

Options to connect the LLM:

Use Glide’s webhook/Airtable automation to send a request to an automation platform (Zapier/Make) which calls the LLM API.
Or use a small serverless function (Vercel/Lambda) if you want more control (still minimal code).

Sample LLM system instructions (use with Claude/ChatGPT):

{
  "system": "You are a concise local dining recommender. Output a JSON array of the top 5 restaurants with the fields: id, name, score (0-100), reason_short (20-30 words), highlight_tags.",
  "user": "Given user preferences: {preferences_json}. Candidate restaurants: {candidates_json}. Return the recommendations only as valid JSON. Preference priorities: dietary > distance > price > rating."
  }

Make sure to request a strict JSON output to avoid hallucinations. Use a few-shot example in your prompt if needed.

Step 5 — Build the group decision flow (30–60 minutes)

When the host shares the session link, everyone joins via the PWA. Use the builder’s list components to create vote buttons. Implementation tips:

Store votes in your Sessions table as simple rows: session_id, user_id, restaurant_id, vote_type (thumbs_up/thumbs_down/skip).
Show live vote counts with Glide’s computed fields or using periodic refreshes.
Tie-break rules: if tied after a 3-minute vote window, use either random selection weighted by votes, or let the host decide. Automate with a Zap that runs the tie-break logic.

Step 6 — Add ordering & ETA paths (30–60 minutes)

Deep links are your friend. Most delivery marketplaces support URL schemes that open the restaurant page or pre-fill. If you have a direct ordering API for your city, connect it via Zapier/Make. Otherwise:

Provide an “Order” button that opens the restaurant’s Uber Eats / DoorDash page or its website/phone number.
Show an estimated wait time using either aggregate stats you store in Airtable (e.g., avg prep time) or by fetching estimated ETAs from partner APIs where available.
Offer a quick split-bill link: pre-built Stripe Checkout session or a simple shared payment link (Stripe Payment Links) with the total and names.

Step 7 — Test, iterate, and deploy (30–60 minutes)

Run 2–3 test sessions with friends. Measure:

Time from session start to decision.
Vote participation rate.
Orders completed / link click-through.

Tune prompts, threshold scoring, and the number of candidates. Deploy the PWA and share via a QR code at your meetup spot.

Prompt engineering examples (ready to paste)

Use these templates to get reliable, structured responses from Claude or ChatGPT.

Recommendation call (JSON output)

System: You are a strict JSON-only recommender for group dining. Always return only JSON. 
User: Preferences: {"cuisine":"Korean","price_level":2,"dietary_tags":["vegetarian"],"max_distance_m":3000}. 
Candidates: [{"id":1,"name":"Bibim House","cuisine":"Korean","tags":["vegetarian","spicy"],"rating":4.2},{...}] 
Return: {"recommendations":[{"id":1,"score":88,"reason":"Vegetarian-friendly bibimbap and quick service (under 15 min)","highlights":["vegetarian","fast"]}, ...]}

Short friendly explanation for the shortlist

System: You are a friendly group assistant.
User: Summarize why each recommended place is a good fit in one sentence for group chat sharing.
Output sample: "Bibim House — tasty vegetarian bibimbap, quick 15-min service, mid-priced."

Advanced upgrades (after day one)

Once the MVP is stable, consider these 2026-forward upgrades:

RAG + vector search: Index menus and reviews for fast, relevant excerpts (use Pinecone/Weaviate + LLM retrieval).
On-device or private LLMs: For privacy, use provider on-device models where available (2026 trends emphasize local inference for private data).
Multimodal inputs: Let users snap a menu photo; use OCR + LLM to extract items and dietary tags.
Reservation & order automation: Integrate with OpenTable-style APIs or marketplace order APIs to create reservations or pre-orders programmatically.
Analytics dashboard: Use Supabase/Metabase to track decision time, favorites, and ordering conversion.

Cost, privacy, and maintenance (practical considerations)

Costs: Airtable/Glide tiers, LLM API calls (batch candidates to reduce tokens), and mapping/places API usage. Keep candidate lists small (20–30) to reduce LLM token costs.
Rate limits: Use caching for repeated queries; refresh recommendations only when preferences change or on demand.
Privacy: Don’t send personal chat content to LLMs. Anonymize user data before calling external models. Offer an opt-out for sending names or contact details to third-party APIs.
Maintenance: Rotate API keys, monitor restaurant data freshness, and keep a manual edit interface for local mom-and-pop spots that aren’t in public APIs.

Mini case study: the Where2Eat vibe-coding pattern

Rebecca Yu’s one-week personal app (Where2Eat) is a great example of the micro app ethos: build for a user group you know, ship quickly, iterate on feedback. In 2026 the pattern is the same but faster — LLMs reduce decision logic to prompt engineering and low-code builders remove UI friction. The result: less debate, faster choices, and more shared meals.

Quick launch checklist (get this live in one day)

Pick your builder: Glide for fastest route.
Set up Airtable/Sheets schema and import 20–30 restaurants.
Create preference form + session creation page.
Wire a webhook to an LLM via Zapier/Make for recommendations.
Build a voting screen and tie-break automation.
Add ordering deep links and a split-payment option.
Test with 3 friends, iterate, then share QR link.

Actionable takeaways

Ship small: A shortlist + vote resolves 80% of group indecision.
Control tokens: Pass only necessary candidate data to the LLM and cache results.
Make it social: Session links and in-app voting create commitment to a choice.
Prioritize data freshness: menus and ordering links are the make-or-break features for conversion.

Final notes: trends to watch in 2026

Expect continued improvements in multimodal LLMs, stronger on-device inference options for privacy-first micro apps, and deeper marketplace APIs unlocking direct ordering and reservations. Micro apps will keep thriving as personal utilities — and dining is a perfect, high-reward use case.

Ready to build? Start with a simple Glide + Airtable prototype and wire one LLM call. You’ll solve 'where to eat' for your friends in under a day and can upgrade features as you learn what your group actually uses.

Call to action: Start your one-day build now — create an Airtable with 20 local spots and paste the recommendation prompt above into a Zapier webhook. Share your micro-app link in the group chat and watch decision time fall to minutes. Got questions about wiring prompts to Claude vs ChatGPT, or need a checklist tailored to your city? Ask here and I’ll help you build the exact flow for your crew.

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