how-toPowerPlatformprototyping

From idea to prototype in 7 days: Build a dining decision micro-app with Power Apps

ssharepoint

2026-02-07

11 min read

Build a dining decision micro-app in 7 days with Power Apps — step-by-step sprint, data model, connectors, Power Fx, and governance tips for 2026.

Beat decision fatigue: build a dining micro-app in 7 days with Power Apps

Hook: If you manage SharePoint, Power Platform, or a team of citizen developers, you know the pain: feature requests pile up, governance demands tighten, and stakeholders still want quick, usable apps. What if you could prove value in one week — not with a bulky project plan, but with a focused micro-app that solves a real problem and validates assumptions?

This hands-on tutorial walks you from idea to working prototype in 7 days. Inspired by the consumer story of Rebecca Yu's Where2Eat, we’ll build a dining decision micro-app using Power Apps, Dataverse (or SharePoint), connectors, Power Automate, and AI-assisted logic. You’ll get a day-by-day plan, a recommended data model, connector patterns, UX guidance, Power Fx snippets, and governance tips for citizen dev programs in 2026.

"Once vibe-coding apps emerged, I started hearing about people with no tech backgrounds successfully building their own apps..." — Rebecca Yu (on building Where2Eat in seven days)

Why micro-apps and rapid prototyping matter in 2026

Micro-apps — lightweight, focused applications built by individuals or small teams — have become a mainstream way to test ideas quickly. By late 2025 and into 2026, enterprise tooling lowered the barrier even further: AI-assisted generators, richer connectors, and better low-code ALM mean prototypes get real feedback faster. For IT and platform owners, the right approach balances speed with governance: enable citizen devs while protecting data and compliance.

What you’ll learn and deliver

A 7-day sprint plan to deliver a dining decision micro-app
A practical data model for restaurants and user preferences
Connector options: Bing/Google Places, Yelp, Microsoft Graph, OpenAI/Azure OpenAI
Power Apps UX patterns and Power Fx snippets for core behaviors
Testing, governance and rollout steps for citizen developers

Day 0: Scope and agreement (0.5 day)

Before you open Power Apps, define the minimal viable outcome. Use this checklist:

Primary user: You + 4 friends. Scope: decide where to eat from 20 local restaurants.
Key flow: choose preferences, get 3 ranked suggestions, vote and finalize a restaurant.
Non-functional constraints: mobile-first, offline not required, simple sharing (link or Teams).
Success metric: users accept top recommendation at least 40% of the time during the first week.

Day 1: Build the data model (4–6 hours)

Choose a data backend. For enterprise proofs-of-concept Dataverse is ideal. For quick personal prototypes, a SharePoint list or Excel in OneDrive works. Below is a recommended Dataverse table design you can translate to SharePoint lists.

Dataverse tables and fields

Restaurants
- Name (Text)
- ExternalId (Text) — map to Google/Yelp ID
- PrimaryCuisine (Choice) — e.g., Italian, Sushi, Mexican
- Tags (MultiSelect Choices / Text)
- Latitude (Decimal), Longitude (Decimal)
- Rating (Decimal) — external rating
- ImageUrl (URL)
- Source (Choice) — Manual, GooglePlaces, Yelp
UserProfiles
- UserId (Text) — Azure AD object id or email
- PreferredCuisines (MultiSelect)
- Dislikes (Text)
- DietaryTags (Text)
Votes
- VoteId (GUID)
- RestaurantId (Lookup Restaurants)
- UserId (Lookup UserProfiles)
- VoteType (Choice) — Upvote/Downvote/Selected
- Timestamp

Tip: use a Choice column for cuisines to simplify filtering and reporting later.

Day 2: Populate minimal data and seed connectors (3–4 hours)

Seed the Restaurants table with 15–30 nearby entries. You can do this manually or automate via connectors.

Connector options (pros/cons)

Google Places: rich local data, but requires API key and quota management.
Yelp API: good reviews and categories; stricter terms and keys.
Bing Maps / Local Insights: often easier to use in Microsoft environments.
Custom scraper or CSV: fastest for prototypes but not scalable.

Example: Power Automate flow to import Google Places (HTTP action)

Create a flow that calls the Google Places Nearby Search endpoint and inserts results into Dataverse. Replace YOUR_API_KEY and environment details.

{
  "method": "GET",
  "uri": "https://maps.googleapis.com/maps/api/place/nearbysearch/json",
  "queries": {
    "key": "YOUR_API_KEY",
    "location": "47.6062,-122.3321",
    "radius": "5000",
    "type": "restaurant"
  }
}

Parse JSON, iterate with an Apply to each, and use the Dataverse connector to create records. For prototypes, batch and limit to 20 results.

Day 3: Sketch UX and build core screens (4–6 hours)

Design three screens in a Canvas app: Home, Preferences, and Results. Keep components minimal and mobile-first.

Screen breakdown

Home: quick start button, recent suggestions, and a group invite link (Teams share or link).
Preferences: choose cuisines, toggle dietary filters, set radius.
Results: ranked suggestions with image, rating, distance, and vote buttons.

Power Fx snippets for common behaviors

Collect user preferences on OnSelect of Save:

Patch(UserProfiles, Defaults(UserProfiles), {
  UserId: User().Email,
  PreferredCuisines: Concat(SelectedCuisines, Value & ";"),
  DietaryTags: Concat(SelectedDietaryTags, Value & ";")
})

Filter and rank restaurant suggestions using a simple scoring function:

ClearCollect(
  CandidateRestaurants,
  AddColumns(
    Filter(Restaurants,
      DistanceFrom(UserLat, UserLng, Latitude, Longitude) <= SelectedRadius
    ),
    "MatchScore",
    If(PrimaryCuisine in SelectedCuisines, 2, 0)
    + If(IsBlank(Find(SearchBox.Text, Name)),0,1)
    + (Rating/5)
  )
);

ClearCollect(TopSuggestions, SortByColumns(CandidateRestaurants, "MatchScore", Descending))

Note: DistanceFrom is a helper function you can implement with Haversine or call Bing Maps distance. For prototypes, approximate distance or omit it.

Day 4: Add connectors and AI-assisted logic (4–6 hours)

Enhance recommendations with two capabilities:

Pull live ratings and photos (Google/Yelp/Bing).
Use an AI scoring layer to translate fuzzy preferences into tags.

AI-assisted preference mapping

In 2026, AI copilots and light-weight LLMs are embedded into low-code tooling. For a prototype, call Azure OpenAI or the Power Platform AI Builder (if available) via a Power Automate flow that: takes user-entered free text (e.g., "I feel like something spicy"), returns normalized tags like "Spicy, Mexican", and maps them to cuisines or restaurant tags.

Power Automate flow example (text -> tags)

Trigger: Power Apps action
Action: HTTP call to Azure OpenAI or external LLM endpoint with prompt: "Normalize this dining preference into 3 tags: ..."
Parse response and return tags to the Canvas app

Back in Power Apps, call the flow and update SelectedTags. That gives you natural language acceptability without complex logic.

Day 5: Voting, group decision, and persistence (4 hours)

Add real-time-ish interaction so friends can vote and finalize.

Voting pattern

User taps Upvote / Downvote — app writes a Vote record to Dataverse or a SharePoint list.
App recalculates aggregated scores with a Power Fx formula that sums votes.
When a consensus reaches a threshold (e.g., 3 selected votes), mark the restaurant as selected.

Power Fx to submit votes

Patch(Votes, Defaults(Votes), {
  RestaurantId: ThisItem.RestaurantId,
  UserId: User().Email,
  VoteType: "Upvote",
  Timestamp: Now()
});

// Recompute local aggregate
ClearCollect(AggScores, AddColumns(TopSuggestions, "AggScore", Sum(Filter(Votes, RestaurantId = ThisRecord.RestaurantId), If(VoteType="Upvote",1,-1))))

Day 6: Test, instrument, and govern (4 hours)

Testing and governance are often skipped by citizen devs — don’t. For a micro-app intended for a few users, keep controls light but measurable.

Testing checklist

Functional: preferences, ranking, voting, persistence.
Auth: Azure AD sign-in if you need identity (Teams + AAD recommended).
Performance: measure response times of your connector calls.
Edge cases: missing images, zero results, API quota failures.

Instrumentation

Enable Power Platform analytics for app usage and errors.
Log votes and errors to a Dataverse table for quick querying.
If calling external APIs, track quota usage in your admin logs.

Governance notes

Check licensing: Dataverse and premium connectors may need per-app/per-user licenses.
Register connectors as approved in your environment to reduce shadow-IT risk.
Keep PII and external keys out of the app — use Azure Key Vault or Power Platform connection references.

Finish UX details and prepare a shareable package. Focus on delight and clarity:

Onboarding overlay that explains a quick flow: pick preferences → get suggestions → vote.
Use large tap targets, contrast, and a single-column layout for mobile.
Tooltips and a help screen describing data privacy (what’s stored, for how long).

Packaging for reuse

Create a solution in Power Apps with your Canvas app, flows, and Dataverse tables. Export as a managed solution for handoff or import into another environment. Document the setup steps for connectors and API keys so other citizen devs or IT admins can reproduce the prototype. Ship templates and a starter scaffold to reduce onboarding friction.

Advanced strategies and architectural trade-offs

When you graduate a micro-app from prototype to production, these decisions matter:

Backend choice: Dataverse vs SharePoint vs SQL

Dataverse: better modeling, choice columns, and relationships; recommended if you need ALM, security roles, or heavier integrations.
SharePoint: fast and cheap for simple lists and smaller audiences; watch for list threshold issues and complex relationships.
Azure SQL: best for large datasets, complex queries and performance-sensitive scenarios; requires more admin work.

Connector and licensing trade-offs

Premium connectors and Dataverse increase per-user costs — document ROI before scaling.
Use cached results and quotas for external APIs to avoid unexpected bills.
Consider an Azure Function as a stable, single place for external API calls and transformations — easier to monitor and secure.

AI usage and trust

AI helps translate fuzzy preferences into actionable tags, but always provide transparency: show AI-derived tags and let users edit them. Log AI decisions for later review to avoid biases and to improve prompts.

UX considerations specific to dining micro-apps

Design for quick consensus. Here are UX patterns that work:

Default shortlist: show 3 options up front — cognitive load drops and decisions happen faster.
Progressive reveal: show essential info first (name, cuisine, rating), allow expansion for menu or photos.
Group tuning: let users add a "must-have" filter (e.g., vegetarian) that removes non-compliant options immediately.
Invite & sync: generate a Teams meeting card or link so friends can join voting in context.

Security, privacy and compliance (short checklist)

Restrict data access with Dataverse security roles or SharePoint permissions.
Do not embed API keys in app code. Use connection references or Azure Key Vault proxies.
Document retention: how long will user votes and preferences be kept?
For corporate environments, route external calls through approved network egress or API gateways.

Measuring success and next steps

After launch, measure the metrics that validate your hypothesis:

Adoption: number of unique users in first week
Engagement: average votes per session
Conversion: percentage of sessions that end with a selected restaurant
Feedback: NPS or simple thumbs up/down on recommendations

Real-world notes: governance in the era of micro-apps (2026 perspective)

By 2026 the balance between speed and control is the central operational challenge for platform teams. AI tools and improved connectors make micro-apps faster but also increase operational surface area. Here are practical rules for platform owners:

Maintain an approved connector catalog and a lightweight certification checklist for citizen-built micro-apps.
Require manageable telemetry: every micro-app should register a usage endpoint so platform admins can track adoption and anomalies.
Offer templates: ship pre-built solutions (Dataverse tables + sample flows + Canvas app shell) so developers start from secure, documented scaffolding.

Actionable takeaways

Use a 7-day plan: Day 1 data model → Day 3 UX → Day 4 AI/connectors → Day 5 voting → Day 6 test → Day 7 ship.
Prefer Dataverse for relational modeling and ALM; use SharePoint for the fastest possible proof-of-concept.
Leverage Power Automate for external API calls; centralize keys in Azure to stay secure.
Keep UX focused: shortlist, simple voting and transparent AI-derived tags.
Document licensing and governance decisions before scaling beyond a few users.

Final words and next experiment

Micro-apps like a dining decision helper can be built in a week with Power Apps and a few supporting pieces. The real value is learning — you’ll discover which data sources matter, which UX patterns cause conversions, and whether your organization can safely enable citizen developers.

Ready to try it? Start with a single-database seed, add one connector, and commit to a weekend of focused building. If you’re in an enterprise context, bring an IT reviewer on Day 3 to validate connectors and auth.

Call to action

Take the 7-day challenge: build your dining micro-app prototype this week and share your results with the SharePoint.News community. If you want a starter solution (Dataverse schema, Power Fx snippets, and a sample Power Automate flow), sign up for the weekly newsletter or request the template in the comments — we’ll publish a reproducible solution for platform teams and citizen devs.

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