case studyno-codemicro-apps

Case Study: Building a Dining Decision Micro‑App with Composer in 7 Days

ccompose

2026-01-28

10 min read

How a creator built a dining micro‑app in 7 days with Composer, LLM prompts, and growth experiments — plus templates to copy.

Hook: From decision fatigue to a launched micro‑app in 7 days

You're a creator who needs polished landing experiences fast: no dev backlog, no fragile integrations, and no slow pages that hurt conversions. That’s the problem Sofía Rivera faced in January 2026 when her group chat could never decide where to eat. This case study shows how she used Composer, modern LLM prompts, and a tight analytics + growth experiment loop to design, build, and launch a dining‑recommender micro‑app—NoshNow—in 7 days. You’ll get the exact timeline, actionable AI prompts, Composer patterns, analytics events, and a launch checklist you can copy.

Why this matters in 2026

Micro‑apps exploded in late 2024–2025 and matured through 2026 as creators demanded fast, personal product experiences. New trends that changed the game:

Edge first and instant pages: Edge compute and prerendered micro‑apps make sub‑200ms time‑to‑interactive standard.
LLM-assisted UI design: Designers and non‑technical creators rely on LLMs and RAG to generate personalized UX patterns and data schemas.
Privacy-first analytics: Server‑side tagging, first‑party data capture, and cookieless metrics replaced brittle third‑party reliance.
No-code + code collaboration: Tools like Composer let creators ship without devs, while offering developer hooks for advanced features.

Overview: NoshNow in 7 days

High‑level timeline. Sofía’s goal: a tiny web micro‑app that recommends restaurants based on a small quiz, shares a 1‑click invite in chat, and captures referrals for growth. She used Composer for page building, an LLM for the recommendation logic, PostHog + GA4 for analytics, and Vercel’s Edge Functions for a lightweight server side that validates referrals.

Day 0 — Idea & constraints (2 hours)
Day 1 — UX & content skeleton in Composer (4–6 hrs)
Day 2 — LLM recommender & prompts (3–5 hrs)
Day 3 — Integrations: email, analytics, serverless (4 hrs)
Day 4 — A/B variant and experiment setup (3 hrs)
Day 5 — Polish, SEO, performance tuning (2–4 hrs)
Day 6 — Beta test with 30 users (2 hrs + feedback loop)
Day 7 — Launch and growth push (social + referral)

Key outcomes in week 1

Launch MVP with single landing micro‑app, average LCP = 0.9s.
Signup conversion 18% on day 1 (goal 10%).
Referral share rate 12% with one simple referral CTA.
Three growth experiments queued for week 2.

Day‑by‑day narrative (what Sofia actually did)

Day 0 — Narrow the scope

Sofía wrote a one‑sentence mission: "Help groups pick a restaurant in under 30 seconds." She constrained the app to three screens: quiz, results, and share. That constraint is critical for speed. Narrow scope = faster shipping.

Day 1 — Build the shell in Composer

Using Composer’s template library, she created a header, a 4‑question quiz component, results cards, and a share modal. Composer’s component system let her reuse a CTA and card component across variants.

Composer tasks she completed:

Duplicate a landing template and strip excess sections.
Build a reusable Quiz component with 4 radio questions (price, cuisine, distance, vibe).
Create Result Card component: image, rating, CTA, quick map link.
Hook up a lightweight CMS collection for restaurants (50 seed entries).

Day 2 — Recommendation logic with LLMs

Instead of writing a full rules engine, Sofía used an LLM to score restaurants based on quiz answers. She chose a hybrid approach: a lightweight scoring function + LLM to rewrite and humanize output.

Why hybrid?

Determinism for ranking (fast, explainable scores) + LLM for copy that increases trust and clicks.

LLM prompt she used (copyable)

System: You are a recommendation assistant. Input: user choices and candidate restaurant fields (cuisine, price, distance_mins, tags, rating). Output: a JSON array of top 3 restaurants with fields: id, score (0-100), snippet (1 sentence), reason (short). Keep output strict JSON.

Example user prompt

USER: {"choices": {"cuisine":"Mexican","price":"$$","distance":15,"vibe":"cozy"}, "restaurants": [ ... ]}

This prompt pattern generated humanized snippets like: "Tapas‑style tacos with bright citrus salsa — 8 min away, $$." Composer calls the LLM via serverless function and merges scores with the CMS list.

Day 3 — Analytics, email, and referral backend

Sofía prioritized observability and growth hooks early. She instrumented the micro‑app with the following:

PostHog for event tracking and funnel analysis (client + server events).
GA4 for aggregate traffic and attribution.
Serverless function on Vercel to validate referrals and write to a small Airtable base.
SendGrid for transactional emails (invite links).

Essential events to track

quiz_started
quiz_submitted (properties: choices)
results_viewed (array of result ids)
share_clicked (method: link|sms|copy)
referral_accepted (referral_id)
signup_completed (email, opted_in)

Tip: Emit quiz_submitted and results_viewed with the same correlation_id so you can reconstruct funnels easily in PostHog.

Day 4 — Growth experiments (A/B test basics)

Sofía launched two simple experiments on day 4 to move the needle quickly:

CTA Wording: "Find a spot now" vs "Get 3 picks in 10s"
Result Order: LLM‑humanized snippet first vs score first

She used PostHog’s feature flags to randomize variants and tracked conversion events. After 48 hours, data showed the "Get 3 picks in 10s" variant increased quiz starts by 22%.

Day 5 — SEO, performance, and accessibility

Composer’s prerendering plus edge delivery made performance simple, but Sofía still followed a short checklist:

Server‑side render the landing page and quiz shell for SEO and faster TTI.
Provide canonical metadata, Open Graph, and structured data for LocalBusiness schema for top results.
Audit images (WebP + width descriptors) and lazy‑load non‑critical assets.
Run Lighthouse and fix any accessibility score < 90.

Day 6 — Beta testing and feedback

She invited 30 friends and tagged them as beta testers. The goal: uncover misaligned recommendations and UX friction. The verbatim feedback was fed back into the LLM prompt (prompt tuning) and the restaurant CMS (add missing tags like "late-night").

"The LLM was great for tone, but we fixed edge cases with explicit tag rules—best of both worlds." — Sofia Rivera

Day 7 — Launch

Sofía launched with a soft social push: an Instagram story showing the app in action, a Twitter thread that described the 7‑day build, and a simple email to her 2k subscribers. She used the referral link as the growth lever: every accepted referral unlocked a freebie (a curated PDF of 10 group-friendly spots).

Concrete assets you can copy (templates & prompts)

Below are ready‑to‑use items: Composer structure, LLM prompt template, analytics event schema, and a 7‑day checklist.

Composer page component map (copy)

Header (logo, small nav)
Hero (one sentence benefit + CTA)
Quiz component (4 questions, progress bar)
Results grid (reusable ResultCard)
Share modal (copy, link + sms + copy button)
Footer (privacy, contact)

LLM prompt template (strict JSON output)

System: You are an assistant that ranks restaurants for a small group using explicit scoring rules and produces humanized snippets. Output must be JSON array:
[ {"id": "r123","score": 0-100, "snippet":"...","reason":"..."}, ... ]
User: {"choices": {"cuisine":"...","price":"...","distance":...},"restaurants": [{"id":"r123","cuisine":"...","price":"$","distance":10,"tags":["cozy","late-night"],"rating":4.5}, ...]}

Analytics event schema (copyable)

quiz_submitted: {user_id, choices, correlation_id, timestamp}
results_viewed: {user_id, result_ids, correlation_id, timestamp}
share_clicked: {user_id, method, referral_id?, timestamp}
referral_accepted: {referral_id, inviter_id, acceptor_id, timestamp}
signup_completed: {user_id, email_hash, referral_id?, timestamp}

7‑day launch checklist (printable)

Define 1‑sentence mission & 3 screen scope
Create Composer page from template
Build CMS seed data (50 restaurants)
Implement LLM scoring + snippet generation
Integrate analytics (PostHog + GA4)
Setup referrals + serverless validation
Run 2 simple A/B tests
Run accessibility and performance audits
Beta test with 20–50 users
Launch with social + email + referral push

Growth experiments to queue (week 2+)

Don’t stop after launch. Sofía prioritized three experiments based on early signals:

Personalized hero — show a snippet using local time or known user preferences (A/B: personalized vs generic).
Social proof — surface local ratings and micro‑testimonials in result cards.
Share incentive — test whether unlocking curated content for 1 accepted referral beats a simple share prompt.

Metrics that matter (and how to read them)

For micro‑apps, focus on a tight funnel. Track these weekly:

Quiz start rate — percentage of visitors who begin the quiz.
Quiz completion → results view — shows friction in the quiz.
Share rate — shares per result view.
Referral accept rate — conversion of shares into new users.
Engagement per invite — retention of users who came through referral.

Use PostHog funnels for step analysis and GA4 for channel attribution. For privacy and accuracy in 2026, add server‑side event validation to reduce bot noise and rely on hashed identifiers for basic user linking.

Lessons learned: practical wisdom from the build

Ship restrictions, not features. Narrow scope to a single user job (pick a place) and optimize that experience instead of adding social feeds or heavy maps.
Hybrid logic beats pure LLM. Deterministic scoring ensures explainability; LLMs add persuasive language and edge case handling.
Events > pageviews. Build event schema first so every change maps to measurable outcomes.
Use Composer components as single sources of truth. Reuse CTAs and card components to keep conversion copy consistent and testable.
Plan for privacy. In 2026, cookieless measurement and first‑party data strategy are table stakes.

Common pitfalls and how to avoid them

Over‑personalizing on day one: rely on simple answers and gradually layer personalization after acquiring consented signals.
Heavy client LLM inference: prefer serverless calls to keep load and costs predictable.
Skipping feature flags: always ship experiments behind flags so you can rollback quickly.
Ignoring accessibility: small apps can still lose big audiences when not accessible.

Advanced pattern: composer + edge functions for real‑time personalization

When Sofía wanted hyper‑local freshness (open now, wait times), she added an edge function that queried a lightweight API and merged results at render time. Pattern:

Composer prerenders static shell for SEO.
Edge function fetches fresh local data during initial request or first interaction.
Client receives merged result and fires events for analytics.

Benefits: fresh data, SEO benefits, and still fast load times.

What changed in late 2025–early 2026 that made this easier

Two real shifts made Sofía’s 7‑day build possible and repeatable in 2026:

LLM pricing and latency improved, and reliable strict‑JSON prompting patterns became standard. This reduced the iteration time for recommendation logic.
No‑code platforms like Composer added developer hooks (edge functions, serverless connectors, and feature flags) so creators could build production‑grade micro‑apps without bespoke infra.

Quick reference: Copyable prompt + Composer checklist

Save this block as your starter kit.

LLM System Prompt: You are a concise restaurant recommender. Input: user choices + candidate restaurants. Output: JSON array of top 3 {id,score,snippet,reason}.

Composer Checklist: 1) Template > minimal hero 2) Add Quiz comp 3) Connect CMS 4) Serverless LLM 5) Analytics 6) Feature flags 7) Launch

Final takeaways

Creators can build useful micro‑apps quickly in 2026 when they combine three things: a narrow product mission, composable no‑code tools like Composer, and a measurement + experiment loop. Sofía’s 7‑day NoshNow launch is repeatable because she focused on constraints, hybrid logic, and measurable experiments.

Call to action

If you want the exact Composer template, LLM prompt file, and analytics event JSON Sofia used, grab the ready‑to‑duplicate pack and a 7‑day launch checklist. Build your own dining micro‑app (or any micro‑app) in a week—no dev team required. Click the button to duplicate the Composer starter and get the prompt pack sent to your inbox.

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