Build a 'Dining Decision' Micro App: From Prompt to Production in Seven Days

Ship a usable dining micro app in seven days — without breaking your sprint

Decision fatigue, messy shared scripts, and inconsistent AI prompts are common pain points for engineering teams and IT admins. If your team wastes hours debating where to eat in a group chat, or your prompt experiments live in scattered notes and repos, this guide is for you. In seven days you'll go from a concise MVP idea to a deployable micro app (a dining app) with reusable scripts for prompt design, data connectors (Google Maps + reviews), UI scaffolding, and CI/CD — built for a small user group and fast iteration.

What you’ll get — the elevator summary (most important first)

• A validated seven-day sprint plan with day-by-day deliverables.
• Reusable prompt templates and a prompt testing workflow for reliable AI recommendations.
• Practical data connector patterns for Google Maps and reviews APIs (including caching and rate-limit strategies).
• UI scaffolding (React/Next) and serverless backend snippets for fast deployment.
• CI/CD and deployment scripts to push to Vercel, Cloud Run, or AWS Lambda for a small beta group.

Why build a dining micro app in 2026?

Micro apps have matured from “fun hacks” to pragmatic tools teams rely on for fast workflows. In late 2025 and early 2026, two trends made this strategy especially powerful:

• Major LLM providers standardized tooling and function-calling, making prompt-driven backends more deterministic and testable.
• Edge and serverless platforms reduced cold-start latency for small user groups, enabling responsive AI-powered experiences without heavy infra.

Combine these with vector DBs for contextual memory and prompt versioning best practices, and you can ship a reliable dining app quickly and iterate safely.

Seven-day sprint — focused, practical, reproducible

Follow this daily plan. Each day has a clear deliverable and small reusable scripts you'll keep in a shared, versioned library.

Day 0 — Prep & goals (2 hours)

• Define the core user story: "Group of 3–6 users want a quick, consensus restaurant for tonight within 15 minutes."
• Decide constraints: geo radius, budget, cuisine filters, open-now, and accessibility needs.
• Set success metrics: conversion to “picked restaurant”, time-to-choice, and # of recommendations per session.

Day 1 — Data connectors & keys (4–6 hours)

Implement connector scripts and test them locally.

1. Register Google Cloud project and enable Maps Places and Geocoding APIs. Use restricted API keys and set HTTP referrers or IP allowlists.
2. Pick a reviews source (Google Places Reviews, Yelp Fusion, or a third-party aggregator). Note rate limits and TOS.
3. Create a connector module (Node/TS) with caching and exponential backoff. Store API keys in Secret Manager or environment variables.

// connectors/googlePlaces.js (Node)
const fetch = require('node-fetch');
const API_KEY = process.env.GOOGLE_PLACES_KEY;

async function searchPlaces(lat, lng, keyword, radius = 1000) {
  const url = new URL('https://maps.googleapis.com/maps/api/place/nearbysearch/json');
  url.searchParams.set('location', `${lat},${lng}`);
  url.searchParams.set('radius', String(radius));
  if (keyword) url.searchParams.set('keyword', keyword);
  url.searchParams.set('key', API_KEY);

  const res = await fetch(url);
  if (!res.ok) throw new Error('Places API error');
  return res.json();
}

module.exports = { searchPlaces };

Day 2 — Prompt design & testing harness (4–6 hours)

Prompt design is the heart of a dining app that produces consistent outputs. Build a prompt test harness so you can iterate quickly and version prompts.

Prompt patterns

• System: Set persona and constraints (e.g., concise suggestions, explain score factors).
• Tool outputs: Feed structured place data (name, rating, price level, distance, cuisine tags).
• User: Provide group preferences: dietary, avoid list, voting scores.

// prompts/recommendation.txt
System: You are a concise dining recommender. Return JSON with keys: topChoices (array), rationale, and reasonScores.

Input: list of places with fields: name, rating, price_level, distance_m, open_now, tags.
UserPrefs: {budget: 'mid', cuisine: ['Thai','Mexican'], aversions:['shellfish'], groupMood: 'casual'}

Task: Rank 3 restaurants best matching the preferences and constraints. Provide a short rationale and a breakdown of score contributions.

Output MUST be valid JSON.

Create a small test harness that runs the prompt against sample place data and asserts JSON validity and stability across minor input changes.

Day 3 — Minimal backend API (6 hours)

Implement a serverless function that coordinates connectors and the LLM prompt. Keep logic thin: gather places → preprocess → call LLM prompt → return recommendations.

// api/recommend.js (Express-like pseudocode)
app.post('/recommend', async (req, res) => {
  const { lat, lng, prefs } = req.body;
  const places = await searchPlaces(lat, lng, prefs.keyword);
  const structured = transformPlaces(places.results);
  const promptInput = { places: structured.slice(0,20), prefs };
  const llmResp = await callLLM('recommendation', promptInput);
  res.json({ choices: llmResp });
});

Day 4 — UI scaffolding (React + Next) (6–8 hours)

Build a single-page flow for quick testing: location input → preference chips → recommendations modal with three picks. Keep it minimal and componentized so you can reuse components in other micro apps.

// components/RecommendationCard.jsx (pseudo)
function RecommendationCard({place, onSelect}){
  return (
    

      
{place.name}
      
{place.tags.join(' • ')} — {place.rating} ⭐
      onSelect(place)}>Pick
    
  );
}

UX tip: show the short rationale the LLM returned and a confidence score so users understand why a place was suggested.

Day 5 — Deployment & CI/CD (4–6 hours)

Automate deploys for your small user group. Use Vercel or Cloud Run for the front end and providers' serverless functions for the backend. Add a GitHub Actions workflow that runs lint/test, builds, and deploys on a protected branch.

# .github/workflows/deploy.yml (excerpt)
name: Deploy
on: [push]
jobs:
  build-deploy:
    runs-on: ubuntu-latest
    steps:
    - uses: actions/checkout@v4
    - name: Setup Node
      uses: actions/setup-node@v4
      with:
        node-version: '18'
    - run: npm ci && npm test
    - name: Deploy to Vercel
      uses: amondnet/vercel-action@v20
      with:
        vercel-token: ${{ secrets.VERCEL_TOKEN }}
        vercel-org-id: ${{ secrets.VERCEL_ORG }}
        vercel-project-id: ${{ secrets.VERCEL_PROJECT }}

Store API keys in secret storage (GitHub secrets, AWS Secrets Manager, or Google Secret Manager) and never check them into the repo.

Day 6 — Beta testing & observability (4 hours)

Invite a small user group (5–20 people). Capture qualitative feedback and metrics:

• Choice completion rate (did they select a recommended place?)
• Time-to-choice
• Prompt stability (how often outputs change for same inputs)

Implement lightweight logging and metrics: count API calls, LLM latency, and connector errors. Add a simple feedback button to collect why a suggestion was rejected.

Day 7 — Iterate, prune, and lock the MVP (4–6 hours)

Ship improvements from beta feedback: tweak prompt weights, add a “randomize” button, and harden caching for the reviews endpoint. Tag a release and prepare a short onboarding doc for the initial users.

Prompt design: templates, testing, and versioning

In 2026, prompt engineering is treated like code: it has versions, tests, and CI gates. Follow these practices.

• Prompt-as-code: store prompts in a repo, reference them by commit hash in backend calls.
• Unit tests: run prompt tests that assert JSON schemas from the LLM outputs.
• Canary deployments: route 5% of requests to new prompt versions before full rollout.

// prompt-test.js (pseudo)
const sampleInput = require('./fixtures/samplePlaces.json');
const prompt = fs.readFileSync('./prompts/recommendation.txt','utf8');

async function runTest(){
  const resp = await callLLMWithPrompt(prompt, sampleInput);
  assertValidJsonSchema(resp, recommendationSchema);
}

Data connectors: practical details and gotchas

Connecting to Google Maps and reviews APIs requires attention to performance, compliance, and cost. These principles apply:

• Bulk vs. live calls: Cache Places responses for short TTLs (1–5 minutes) and cache review text less frequently (10–60 minutes) depending on freshness requirements.
• Rate limits: Implement exponential backoff and a queue for burst traffic. For small groups you can often stay well under quotas.
• Privacy: minimize PII sent to LLMs. Send structured place metadata rather than full user messages when possible.

Example: a connector that fetches place details and normalizes tags for prompt consumption.

// connectors/transformPlaces.js
function transformPlaces(rawResults){
  return rawResults.map(p => ({
    id: p.place_id,
    name: p.name,
    rating: p.rating || 0,
    price_level: p.price_level || 0,
    distance_m: calcDistanceMeters(p.geometry.location),
    open_now: p.opening_hours?.open_now || false,
    tags: deriveTags(p.types || [], p.name)
  }));
}

UX & iteration: how to keep users engaged

UX in a micro app is about reducing friction. For a dining app that means:

• Minimal inputs: sliders and chips, not free-form text for preferences.
• Transparency: show why a place was recommended (score breakdown).
• Iteration hooks: allow quick feedback (thumbs up/down) and capture it to reweight prompts.

“Show the reasoning — users trust AI more when you surface the why.”

Security, compliance, and cost control

For small groups, the biggest risks are leaked API keys and unexpected API bills. Mitigate them with:

• Restricted API keys and per-environment quotas.
• Server-side calls to external APIs — never call external keys from the client; consider a proxy or gateway for controlled access.
• Monitor LLM token usage and set daily caps; run cost alarms in cloud billing. For supply-chain and pipeline security, consider red‑teaming your prompt and connector flows (see case studies on red team supervised pipelines).

Reusable scripts and templates (a starter library)

Store these artifacts in a single cloud repo or a scripting platform so team members can re-run, adapt, and version them:

• connectors/googlePlaces.js — place search & details with caching
• connectors/reviewsAdapter.js — unified reviews schema for multiple providers
• prompts/recommendation.txt — base prompt with JSON output contract
• tests/prompt-tests.js — prompt unit tests and JSON schema assertions
• deploy/vercel.yml and .github/workflows/deploy.yml — CI/CD deploy pipelines
• infra/secret-setup.sh — helper for registering secrets in Secret Manager

Observability and iteration: measure what matters

Set up three dashboards:

1. Product: completion rate, time-to-choice, and NPS snippets from feedback.
2. System: connector latency, LLM latency, error rates, API quota usage. (See notes on observability and incident response for practical monitoring playbooks.)
3. Prompt stability: track how often outputs change for the same input and maintain a version history.

Advanced strategies you can adopt after MVP

• Personalization via lightweight embeddings: store group preferences as vectors to retrieve best-fitting restaurants quickly. For edge inference and small-device performance, see benchmarking notes on the AI HAT+ 2.
• Function calling and tools: use model call tools to let the LLM request additional data (menus, reservation links) on demand.
• Offline-first UX: cache the last recommendations locally to make the app resilient in spotty mobile networks.

Case study snapshot (replicable in your environment)

Rebecca Yu’s quick Where2Eat-style iteration inspired many micro apps in 2024–2026. The reproducible elements you can copy from that lineage are:

• Start with a small, real problem (where to eat tonight).
• Use LLMs to synthesize structured API results, not to scrape the web.
• Keep the first release simple: 3 recommendations, one-click accept, and a fallback randomize option.

Actionable checklist — ready-to-run

1. Day 0: Define user story and success metrics. Tag repo with sprint name.
2. Day 1: Create Google project, enable Places API, and add key to secret store.
3. Day 2: Add prompt templates + test harness; write schema assertions.
4. Day 3: Build serverless recommend endpoint and add connector scripts.
5. Day 4: UI scaffolding with RecommendationCard and rationale display.
6. Day 5: Add CI/CD, secrets, and small-team rollout branch protections.
7. Day 6–7: Beta test, collect feedback, iterate, and tag release.

Key takeaways

• Fast iterations win: prioritize working end-to-end over building perfect features.
• Prompt-as-code: version prompts, write tests, and canary new prompt logic.
• Data connectors matter: caching, backoff, and unified schemas make LLM inference reliable and cost-effective.
• Small user groups are forgiving: you can learn a lot from 5–20 users before scaling.

Next steps — try the reproducible artifacts

If you want to reproduce this case quickly, clone a starter repo that contains the connectors, prompt templates, UI scaffolding, and CI pipelines. Host secrets in your cloud secret manager, run the prompt tests, then deploy to a preview environment and invite your first beta users.

Ready to centralize scripts, version prompts, and deploy confidently? Start a free trial at myscript.cloud to import and run the reusable scripts from this guide, manage prompt versions, and connect your CI/CD in minutes. Your dining micro app — and the next micro app your team needs — should take days, not months.

Call to action

Spin up the starter repo, run the prompt tests, and deploy to a preview environment today. Sign up at myscript.cloud to access the ready-made script library and a guided seven-day sprint checklist tailored for engineering teams and IT admins.

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• Proxy Management Tools for Small Teams — when to add a proxy/gateway for API keys and controlled access.
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