Build a Micro App in a Weekend: From Chat Prompts to a Shipping Dining Recommender

Beat decision fatigue: ship a dining micro app in a weekend

You're tired of the endless group-chat debate over where to eat. You want something fast — a usable MVP you and your friends can actually use tonight. This guide walks you through building a micro app dining recommender in a weekend: prototype with LLMs + no-code, then add a small backend, RAG, and deploy. It mirrors the spirit of Rebecca Yu’s seven-day build but is tailored to developers aiming for rapid, repeatable MVPs for friends and teams.

The why (2026 context): micro apps, LLM-assisted development, and the new prototyping stack

By early 2026, two trends make this approach the fastest path from idea to value:

• LLM-assisted development matured: prompt engineering and RAG are mainstream; developers stitch models into workflows instead of hand-coding every decision layer.
• No-code + low-code platforms offer production-grade UI and connectors (Airtable, Glide, Softr, Retool) that eliminate large frontend costs for micro apps.
• Lightweight backends and edge/serverless (Vercel, Supabase Edge Functions, Cloudflare Workers) make single-developer deployments trivial.

Combine these and you can iterate a working recommender in a weekend and ship a small backend in the next day.

High-level plan (what you’ll build)

We’ll follow a 2-phase roadmap you can complete across a weekend + one deployment day:

1. Saturday — Prototype with no-code + LLMs: minimum friction UI, store restaurants in Airtable, use an LLM for prompt-based recommendations.
2. Sunday — Add personalization and testing: collect user preferences, tweak prompts, add RAG with an embeddings/vector DB for locality and menu data.
3. Next day — Ship a tiny backend: move logic to a serverless function, add auth (email/OTP), and deploy on Vercel or Supabase.

Step 0 — Setup: tools I recommend (fast, inexpensive)

• Airtable (free tier) — quick DB for restaurants & users
• Glide or Softr — no-code front-end for prototypes
• OpenAI / Anthropic / Llama-based API — LLM for recommendations
• Supabase or Vercel (serverless) — small backend and auth
• Qdrant or Supabase Vector (optional) — vector DB for RAG
• Postman / curl — quick API testing

Step 1 — Wireframe & Minimal Scope (0.5–1 hour)

Resolve scope aggressively. For a dining recommender, the minimal screen set is:

• Group landing: quick pick or create group
• Preferences: dietary, cuisine vibes, price range
• Recommendation: one recommended restaurant + fallback list
• Feedback: thumbs up / thumbs down to learn

Make the MVP's success metric explicit: "Within the first weekend, 5 friends can open a link and pick a restaurant recommended by the app."

Step 2 — Data model (Airtable schema)

Start with three tables:

• Restaurants: name, address, cuisine, price_level (1-4), tags, short_description, menu_blob (optional), lat, lon
• Users: name, email (or phone), preferences (multi-select), group_id
• Groups: group_name, member_ids, last_recommendation_id

Populate 30–50 restaurants manually or import a CSV. Enough to make recommendations feel real.

Step 3 — LLM prompt design (the heart of quick wins)

Most of the magic comes from good prompts. Use LLMs for two things:

1. Translate user preferences + context into a scoring function for restaurants
2. Generate compact, friendly recommendation text

Example prompt template:

System: You are a concise dining recommender. Prefer short, friendly suggestions with 1-2 reasons.

User: Given the following group preferences and a list of restaurants, pick the single best recommendation and two alternatives. Return JSON:
{
  "choice": {"id": "", "name": "", "reason": ""},
  "alternatives": [{"id": "", "name": "", "reason": ""}]
}

Group preferences: {preferences}
Restaurants: {restaurant_list}

Always ask the model to return JSON so parsing in your no-code tool or function is trivial.

Step 4 — No-code prototype (4–6 hours)

Use Glide or Softr connected to your Airtable base. Steps:

1. Connect Airtable tables to the no-code app.
2. Create a simple form where a group creates a session and members select preferences.
3. Add a button that triggers a webhook (Glide has webhooks or use Zapier/Make) to call your LLM recommendation endpoint.
4. Show the recommended restaurant and two alternatives on a results screen. Use the LLM's reason strings to explain choices.

Pro-tip: keep the UI deliberately minimal. Your MVP's value is recommendations, not pixel-perfect design.

Step 5 — Quick LLM endpoint (Saturday night)

Before building a backend, create a lightweight serverless function you can call from Zapier/Make or the no-code platform. Example Node.js serverless (Vercel function):

export default async function handler(req, res) {
  const { preferences, restaurants } = req.body;
  const prompt = buildPrompt(preferences, restaurants);
  const llmResp = await fetch('https://api.openai.example/v1/chat/completions', {
    method: 'POST',
    headers: { 'Authorization': `Bearer ${process.env.OPENAI_KEY}`, 'Content-Type': 'application/json' },
    body: JSON.stringify({ model: 'gpt-4o-mini', messages: [{ role: 'system', content: '...' }, { role: 'user', content: prompt }] })
  });
  const data = await llmResp.json();
  const recommendation = safeParseJson(data.choices[0].message.content);
  res.json(recommendation);
}

Note: adjust the URL and model name to your provider. This function is a thin wrapper that runs your prompt and parses JSON.

Step 6 — Add personalization & RAG (Sunday)

After you have a working prototype, add two improvements that matter most:

1) Personalized scoring with user history

Store feedback (thumbs up/down). Use it to bias future recommendations. A simple approach:

• When user thumbs-up a recommendation, save {user_id, restaurant_id, delta: +1} in a history table.
• Aggregate recent history per user and boost restaurants with positive signals in the prompt ("Add +2 weight to restaurants X, Y").

2) Retrieval-augmented generation (RAG) for local context

Use embeddings to store restaurant descriptions, menus, and user reviews in a vector DB. At query time, retrieve the top-K nearest restaurants and pass them to the LLM instead of a huge list.

Architecture sketch:

1. Precompute embeddings for restaurants (menu text, tags).
2. On request, embed the group's preference text and query nearest restaurants from Qdrant / Supabase Vector.
3. Pass those top results to the LLM prompt for scoring.

This reduces token costs and gives the model concrete local context (menus, neighborhood, special offers).

Step 7 — Move logic to a small backend and add auth (next day)

Once you confirm the UX, move recommendation logic into a proper backend so you can iterate safely and store signals.

Backend choices

• Supabase (Postgres + Edge Functions) — easy auth and DB
• Vercel + PlanetScale — serverless + scalable MySQL
• Railway or Fly.io — simple deploy for small teams

Minimal endpoints:

• POST /recommend — accept group_id & preferences, return recommendation
• POST /feedback — record thumbs up/down
• GET /restaurants — admin-only sync endpoint

Example /recommend with RAG (pseudo-code)

async function recommend(group_id, prefs) {
  // 1. retrieve group members & aggregated prefs
  const group = db.getGroup(group_id);

  // 2. create a query text
  const queryText = buildQueryText(group, prefs);

  // 3. embed and fetch top restaurants
  const emb = await embedModel.embed(queryText);
  const top = await vectorDB.search(emb, { k: 10 });

  // 4. call LLM with top restaurants
  const prompt = buildPrompt(prefs, top);
  const llmResult = await llm.call(prompt);

  return parseResult(llmResult);
}

Step 8 — Prototype testing and feedback collection

Use the following lightweight testing loop to validate the UX in days, not weeks:

1. Invite 5 friends to a shared group on your app.
2. Ask them to add preferences and run a recommendation session.
3. Collect quick feedback (3 questions): Did you like the top choice? How relevant were reasons? Would you use it again?
4. Update prompt weights, add or remove tags in the restaurant DB based on feedback.

Quantify success: track conversion (click-through to map/phone) and feedback ratio (thumbs up / total). Aim for >60% thumbs-up on first 10 sessions.

Advanced strategies (2026 trends you can adopt)

1) On-device LLMs for privacy & latency

With smaller LLMs and hardware acceleration widespread in 2026, you can run inference on-device for sensitive groups. Use on-device models for preference matching and cloud LLMs for creative reasons.

2) Tools and external actions

Recent tool-augmented agent patterns let models call external APIs (booking, map directions). Use a safe tool layer so the model returns structured calls (e.g., {action: "open_map", coords: ...}).

3) Continuous small experiments

Micro apps are perfect for rapid A/B tests. Try two prompt variations and measure thumbs-up rate. The low friction of micro apps makes rapid iteration easy.

Security, privacy, and cost control

Even micro apps need guardrails:

• Rate limits on serverless endpoints to prevent cost blowouts.
• Data minimization: store only what's needed for personalization.
• Graceful fallbacks: if the LLM fails, return a simple deterministic ranking by proximity and price.

Tip: Cache common prompts/responses and reuse embeddings to reduce API spend.

Example rollout checklist (one day to deploy)

1. Move LLM calls to a serverless function with environment secrets
2. Add email/OTP auth using Supabase or Clerk
3. Enable a vector DB and precompute embeddings
4. Deploy frontend (Glide/Softr to a public link or host a static site on Vercel)
5. Publish a clear privacy note in your app describing what is stored
6. Invite a small circle and monitor analytics

Real-world notes & lessons learned

Rebecca Yu turned a week off into Where2Eat — a personal micro app. The takeaway: start small, iterate with real users, and use today’s AI building blocks to accelerate non-linear progress.

From working on dozens of weekend MVPs, common observations:

• Most early wins come from prompt design, not model choice.
• Users forgive simple UIs if the core recommendation is clearly helpful.
• Collecting even a handful of feedback points dramatically improves personalization (predictive personalization).

Actionable checklist (what to do this weekend)

1. Friday night: sketch the wireframe and populate Airtable with 30 restaurants.
2. Saturday morning: build the no-code UI and connect to Airtable.
3. Saturday evening: create a serverless function that calls an LLM and returns JSON recommendations.
4. Sunday morning: add simple feedback collection and store thumbs up/down.
5. Sunday afternoon: run a test session with friends, iterate on prompts, and record metrics.
6. Next day: harden the backend (auth, caching) and deploy publicly.

Quick reference: sample prompt (ready to paste)

System: You're a concise dining recommender. Pick one restaurant for this group and provide two alternatives. Output strict JSON.

User: Group preferences: "{preferences}". Restaurants (id,name,cuisine,price,tags): {restaurant_list}

Return:
{
  "choice": {"id":"","name":"","reason":""},
  "alternatives": [{"id":"","name":"","reason":""}]
}

Final thoughts — why micro apps are a sustainable strategy in 2026

Micro apps are the fastest way to convert day-to-day friction into value. With LLMs, no-code UIs, and serverless backends, individual developers can ship functional, delightful tools without months of engineering. The goal is not to build the next unicorn — it’s to create useful, maintainable utilities for you and your circle.

Next steps & call-to-action

Try this weekend: build a Where2Eat for your friend group. Use the checklist above, start with Airtable + Glide, and move logic to a serverless function when you’re confident. Share your prototype with the community to get feedback and iterate quickly.

Want a downloadable starter repo (serverless function + example prompts + Airtable schema)? Reply below or visit our project templates page to grab a ready-made kit and deploy in minutes.

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