A Practical Playbook for Building a 7-Day Micro-App for Group Dining

Stop wasting time deciding where to eat — build a 7-day micro-app that fixes group dining decisions

If your team or guests spend 20–40 minutes arguing over dinner choices, you’re losing productive time and goodwill. For operations leaders and small-restaurant owners, that friction also means lower conversion on group orders and unhappy guests. This playbook, modeled on Rebecca Yu’s seven-day build of a group-dining app, gives you a practical, low-risk path to a working micro-app MVP that recommends restaurants for groups, syncs a digital menu, and ships in one week.

Why build a micro-app in 2026?

Micro-apps — small, focused applications intended for a specific problem or audience — exploded between 2023–2025 as modern LLMs, low-code tools, and API ecosystems matured. By late 2025 and into 2026, two trends make now the right time:

• LLM-driven prototyping: Models like ChatGPT-4o and Claude 3.5 (2025–2026 releases) can generate robust prompt-driven logic, SQL snippets, and UX copy in minutes.
• API-first integrations: POS, reservation, and mapping platforms now offer stable, well-documented REST and GraphQL endpoints with webhook support — lowering integration time.

For restaurant operators, a micro-app MVP can be a testbed for a full SaaS rollout: validate concept, measure demand, and iterate on menu conversion using real-world analytics.

What you'll ship in 7 days (minimum viable scope)

Keep scope intentionally narrow. For your group-dining micro-app MVP, ship these core features:

• Group profile: add diners and basic preferences (budget, cuisine, distance).
• Recommendation engine: rank 6–10 nearby restaurants using weighted preferences.
• Digital menu snapshot: show a compact menu (top categories, items, prices).
• Quick vote and tie-breaker: allow group to vote and let an algorithm break ties.
• One-click directions or reservation link (via Google Maps/OpenTable/your POS).

Day-by-day playbook overview

The week is structured to maximize velocity: plan, prompt-engineer the logic, build minimal UX, integrate essentials, test with real users, and launch. Expect 6–12 hours per day if you’re solo; 3–6 hours per day with a two-person team (developer + designer/product owner).

Day 0 — Pre-planning (3–6 hours)

Do this before Day 1 to keep the week focused.

• Define your success metrics: time-to-decision, vote completion rate, menu click-through rate, and NPS among test users.
• Decide hosting and stack: Vercel/Netlify for frontend, serverless functions (AWS Lambda/Cloud Run) for backend, and an LLM endpoint (OpenAI/Anthropic) for prompts.
• Gather APIs: Google Places/Maps, OpenTable/Resy, and your POS if available. If POS is unavailable, prepare a CSV menu snapshot.

Day 1 — Planning & data model (6–8 hours)

Map the minimum data model and user flow. Keep models flat and small.

1. Entities: GroupSession, User (name, dietary preferences), Restaurant (name, cuisine, rating, distance, tags), MenuItem (title, price, category).
2. Flow: Create session → invite members (link/QR) → collect preferences → generate recommendations → vote → choose → action (directions/reserve).
3. Wireframes: Use a 3-screen wireframe: Landing/Invite, Recommendations + Menu, Vote + Details.

Day 2 — Prompt engineering & recommendation logic (6–8 hours)

Prompt engineering is the heart of a fast LLM-driven MVP. Treat the LLM as a deterministic scoring and copy engine, not a database. Lock down templates and guardrails.

Core prompt patterns

Design three prompt roles: system, aggregator, and responder.

// Example (pseudo) for ChatGPT-style API
System: You are a recommendation engine. Return a JSON array of 6 restaurants with score and explanation.
User: Here are group preferences: [list]. Here are candidate restaurants: [list]. Rank them and include short reason and three tags.

Concrete example (short):

System: You are a restaurant recommender for groups. Use these weights: dietary match 35%, budget 20%, distance 15%, popularity 20%, novelty/fun 10%. Don't hallucinate; only use input restaurant data.
User: Group preferences: {"dietary":["vegan"], "budget":"$30-50", "max_distance_km":5}
Candidate restaurants: [{"name":"Green Table", "cuisines":["Vegan","American"], "avg_price":30, "rating":4.5, "distance_km":2.1}, ...]
Return JSON: [{"name":"Green Table","score":0.92,"reason":"Matches vegan preference; mid-range prices; walking distance","tags":["vegan","casual","nearby"]}, ...]

Tips:

• Supply structured data: Always pass restaurant data as JSON to avoid hallucinations.
• Define scoring weights: Hard-code them in the system prompt so the LLM is consistent.
• Result format: Request strict JSON and validate at the application layer.

Day 3 — Minimal UX & prototype (6–10 hours)

Ship lightweight, mobile-first screens. Prioritize clarity and fast interactions over polish.

UX checklist (must-have)

• One-tap session creation + shareable invite link or QR.
• Simple preference capture: sliders (budget), toggles (dietary), and radio buttons (distance).
• Card-based recommendations with score, reason (1–2 lines), and buttons: View menu / Vote / Directions.
• Compact digital menu: top 6-12 items per category, price, and an add-to-order placeholder.
• Voting flow: anonymous counts with visible progress bar. Auto-break ties with LLM logic or host override.

Tools to accelerate UX:

• Component libraries: Tailwind + Headless UI, or MUI for fast prototypes.
• No-code for UI: Figma for layouts and Webflow for a static prototype if you need stakeholder demos.

Day 4 — Integrations (6–10 hours)

Connect only what unlocks value: mapping, reservations, and analytics. Defer POS checkout until you validate demand.

Priority integrations

• Maps & directions: Google Maps or Mapbox for geocoding and directions links.
• Reservations: OpenTable/Resy deep-links or APIs for availability.
• Menu data: If POS available, pull menu via POS API; otherwise ingest CSV or use Google Business Profile menu schema.
• Auth & invites: Passwordless email or magic links to reduce friction; WebAuthn optional.
• Analytics: Segment/Amplitude/GA4 for event tracking (session_create, vote, click_menu, conversion).

Integration tips:

• Use webhooks to get reservation confirmations and update session state in real time.
• Cache third-party data and include a timestamp to avoid excessive calls and LLM hallucinations.

Day 5 — Testing and guardrails (6–8 hours)

Now validate reliability and protect against LLM errors and privacy issues.

Functional testing

• JSON schema validation on all LLM outputs.
• Edge-case tests: missing menu prices, multiple dietary tags, far-distance results.
• Load test: simulate 50 concurrent group sessions to check cold-start latencies.

AI guardrails

• Reject outputs that reference facts not present in the input restaurant data.
• Rate-limit LLM calls and cache prior results for identical inputs.
• Provide a human override flow for host/admin to edit recommendations before sending to group — consider augmented oversight patterns for supervised systems.

Accessibility & privacy:

• Use semantic HTML, alt text for images, and test with Lighthouse for accessibility score.
• Collect minimal personal data and disclose how preference data will be used in a short privacy note.

Day 6 — Beta testing with real users (4–8 hours)

Recruit 5–15 test groups that reflect your target audience: coworkers, family groups, local customers.

• Observe: time-to-decision, drop-off points, and menu clicks.
• Collect qualitative feedback: Was the recommendation persuasive? Did the menu feel accurate?
• Measure quantitative signals: vote completion %, average session time, CTA rate (directions/reserve).

Iterate rapidly: fix the top three usability issues same day and re-run another short test.

Day 7 — Launch and immediate growth tactics (4–6 hours)

Soft launch to a limited audience first, then expand. Use clear calls-to-action that align with your business goals.

• Host on a CDN-backed platform (Vercel, Netlify) to reduce latency.
• Implement a simple onboarding microcopy: how to invite friends, how votes work, and privacy expectations.
• Promote via QR codes printed on receipts or table tents in your establishment — a proven low-friction growth channel for group dining.

Prompt engineering playbook (detailed)

A reliable prompt structure separates intent, logic, and output format. Reuse these templates in your app codebase and CI tests.

System prompt (stability)

Define role, scoring weights, and hallucination rules.

System: You are a deterministic recommender. Use only the provided restaurant JSON. Apply weights: dietary_match=0.35, budget=0.20, distance=0.15, rating=0.20, novelty=0.10. Output strictly valid JSON. If unsure, return best-effort low-confidence flag.

User prompt (context)

Supply group preferences and candidates. Ask for reasons limited to 20 words per restaurant.

User: Group preferences: {...}
Candidate restaurants: [...]
Return: [{"name":"","score":0.0,"reason":"","tags":[],"confidence":"low|high"}, ...]

Validation & safety

• Post-LLM: Validate JSON schema and discard entries with missing fields.
• If confidence=low for top result, present top-3 as choices rather than auto-selecting.

UX checklist — the 10-point quick audit

1. Mobile-first layout: single-column, large tap targets.
2. Fast session creation: < 10s from load to invite link.
3. Clear preference inputs: use defaults to minimize typing.
4. Readable recommendation cards: score, one-line reason, 2 CTAs.
5. Compact menu: show 6–12 items, avoid full menu dumps.
6. Voting visibility: live counts but anonymized.
7. Fallback paths: manual search if recommendations fail.
8. Performance: initial load < 2s on mobile 4G.
9. Analytics: track funnel events and user properties.
10. Privacy: require minimal personal data and show a short privacy note.

Integrations & ops considerations

Short-term integrations boost perceived value; long-term integrations improve operations.

• Short-term: Google Maps deep-links for directions, OpenTable links for reservations, static menu CSVs.
• Mid-term: POS read-only menu sync (to keep prices accurate) via middleware. Webhooks for reservation confirmations.
• Long-term: Bi-directional POS integration for live inventory and contactless order capture.

Operational best practice: keep a sync log and version menu snapshots daily. Each session should show menu timestamp so users understand freshness.

Testing checklist (must-run before any public launch)

• LLM output schema tests (automated).
• End-to-end flows: create → invite → vote → finalize.
• Network failure modes: test offline and slow networks with cached UI.
• Security: basic pen test for injection via inputs, validate serverless endpoints.
• Accessibility: keyboard nav and screen reader basics.

KPIs to measure in the first 30 days

• Conversion rate: sessions that reach final decision / sessions created.
• Time-to-decision: median time from session create to final vote.
• Menu engagement: menu clicks per session and add-to-order placeholders.
• Retention: repeat sessions per organizer per month.
• Revenue proxy metrics: reservation click-throughs and foot traffic lift (if you can measure).

Real-world example: Rebecca Yu’s quick-build lessons

"When I had a week off before school started, I decided it was the perfect time to finally build my application..." — Rebecca Yu, Where2Eat

Key takeaways from Rebecca’s approach that apply to commercial micro-apps:

• Start personal, then widen scope: she built for friends; you should build for your customer segments.
• Use LLMs as glue: she combined Claude and ChatGPT for different tasks — use the right model for ranking, copy, and data transformation.
• Deploy quickly and iterate: a live app surfaces real preferences you can’t predict in planning.

2026 trends and future-proofing

Expect these developments to shape how you evolve the micro-app beyond the MVP:

• Multimodal LLMs: Support image menus and scanned receipts to auto-extract items via OCR+LLM pipelines.
• Vector search personalization: Store session vectors to personalize suggestions across sessions — pair this with observability and experiment tracking (observability for workflow microservices).
• Federated POS integrations: Industry push toward standardized menu schemas and permissioned API models will reduce custom connectors — see notes on Open Middleware Exchange.
• Privacy-first AI: Increasing regulation means collecting minimal PII and offering opt-outs for preference profiling.
• Agent automation: Allow a scheduled “groupPlanner” agent to periodically suggest weekly lunch spots for teams — modeled on AI-assisted support patterns in operations stacks (resilient ops & AI-assisted support).

Advanced strategies after launch

• AB test recommendation weights: use continuous experiments to tune scoring for your audience (pair with observability).
• Monetization experiments: featured listings for restaurants, reservation commissions, or a premium host mode with advanced filters.
• Operational integration: if you own multiple locations, integrate occupancy and wait-time signals to improve recommendations.

Cost and resourcing estimate

Rough budget for a lean 7-day build (USD):

• ODC/frontend + backend dev: $0 if internal, or $1,500–$6,000 for a short freelance sprint.
• LLM API usage: $50–$400 depending on model and traffic during testing.
• Hosting & third-party APIs: $20–$200/month in early stages.

Compare this with off-the-shelf SaaS deployments: micro-apps let you validate concept for a fraction of the cost and time.

Common pitfalls and how to avoid them

• Too much scope: resist adding checkout or POS integration on Day 1.
• Relying on LLM facts: always validate LLM output against structured inputs.
• Poor UX for voting: make voting a one-step interaction and show progress to reduce drop-off.
• Not measuring the right signals: focus on time-to-decision and conversion rather than vanity metrics.

Checklist to ship in 7 days (one-page)

• Day 0: stack, APIs, KPIs defined.
• Day 1: data model & wireframes ready.
• Day 2: LLM prompts and scoring logic implemented.
• Day 3: basic UX prototype built.
• Day 4: core integrations connected.
• Day 5: tests & guardrails implemented.
• Day 6: beta sessions and feedback loop closed.
• Day 7: soft launch and growth kick-off.

Final actionable takeaways

1. Ship a narrow, high-value flow: session → recommendation → vote → action.
2. Use LLMs for ranking & copy, not as a source of truth — always feed structured data.
3. Measure time-to-decision and conversion as your north-star metrics.
4. Integrate minimally at first (maps & reservations), then expand to POS when validated.
5. Protect against hallucination with strict JSON schemas and confidence flags.

Call to action

If you’re evaluating digital menu and group-dining solutions for operations, start with a 7-day micro-app prototype. Use the templates and checklists in this playbook to de-risk the build and measure impact quickly. Need a starting kit — prompts, JSON schemas, and a starter repo tailored to restaurants? Request our 7-day micro-app starter pack for restaurants and accelerate your pilot.

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