case studymicroappsprototyping

Case Study: Rapid Prototyping — How One Non-Developer Built a Dining App in 7 Days

ppromptly

2026-02-01

10 min read

How a non-developer built a dining micro app in 7 days using Claude, no-code glue, and reproducible prompts.

Hook: From idea to working micro app in 7 days — even if you are not a developer

Decision fatigue, split group chats, and feature bloat are daily realities for technology teams and product owners. You know the pain: building and shipping a small, reliable prompt-driven feature feels slow and governance-heavy. What if a non-developer could prototype a real, deployable micro app in a week and hand a reproducible asset to engineering? That is precisely what happened in late 2025 when Rebecca Yu built Where2Eat using Claude, ChatGPT, no-code tools, and a focused iteration cadence. This case study deconstructs the micro-app lifecycle she used, the exact prompts and patterns, the pitfalls to avoid, and production-ready deployment choices for 2026.

Why this case study matters in 2026

By 2026 the AI tooling landscape has shifted from solo model experiments to integrated developer workflows. Prompt registries, PromptOps, and higher-context assistant APIs became mainstream in late 2025. That makes micro apps more than toys: they are low-risk, high-velocity experiments that feed product discovery, reduce backlog clutter, and produce reusable prompt assets for enterprise pipelines. This case study shows how a non-developer prototyped an MVP dining recommendation app in 7 days and delivered artifacts that engineering can reuse.

Project summary

Project: Where2Eat, a group dining recommender
Creator: Non-developer product manager
Duration: 7 days
Stack: Claude and ChatGPT for prompts, Glide for front end, Airtable for data, Pinecone for vector search, Netlify for hosting (variants: Retool or Vercel)
Outcome: Working web app for small groups, deployable beta, documented prompts and prompt versions

The micro-app lifecycle: 7-day sprint blueprint

Micro apps benefit from a compressed lifecycle. Below is a reproducible 7-day sprint that mirrors Rebecca Yu's cadence but adds governance and production hooks useful for technology teams.

Day 0: Define scope and success criteria (2 hours)

Before a single prompt, write a one-paragraph spec with acceptance criteria. Keep scope intentionally tiny: one core use case, two input types, and one output format.

Core use case: Given 3 participants with short preference phrases, return 5 ranked restaurant suggestions with a 1-line rationale each
Success metrics: initial user satisfaction >= 70% in quick testing, median response latency <= 1.5s for model calls, cost <= $10/day at 50 sessions

Day 1: Prompt design and system prompt drafting (4 hours)

This is where most projects stumble. Treat prompts like code: version them, test them, and keep them modular. Create a system prompt and a small set of task prompts. Save them in a prompt registry or a versioned file.

Core system prompt pattern

System: You are a concise dining recommendation assistant. Output must be JSON with keys: recommendations (list of restaurants), rationale (list of 1-line reasons), confidence (0-1). Prioritize common cuisine preferences and distance when included. Avoid hallucinations. If data is missing, ask one clarifying question.

Keep system prompts short and prescriptive. Use the assistant's output format to make downstream parsing deterministic.

Day 2: Prototype data model and simple datastore (4 hours)

Avoid building a full backend. Use Airtable or Google Sheets for structured data, and a vector DB like Pinecone or Weaviate for fuzzy matching if you plan to include menu text or reviews. For Where2Eat, an Airtable base with fields name, cuisine, tags, location, average price, and embedding vector was sufficient.

Day 3: Glue code using no-code or low-code (6 hours)

Glue components:

Front end: Glide, Bubble, or a simple static site with a form
Orchestration: Make (Integromat), Zapier, or a tiny serverless function on Netlify or Vercel
Model API: Call Claude or ChatGPT using the official API key stored in a secrets manager

Day 4: Iteration and user testing 1 (evening)

Invite 5 friends for a guided session. Observe instead of asking. Measure the three most important metrics: correctness, clarity, and time to decision. Capture failure cases as concrete test inputs for Day 5.

Day 5: Fix prompts and handle edge cases

Based on feedback, change one variable at a time. Use targeted prompts for clarifying inputs and for constraint enforcement (e.g., budget limits, distance radius). Record each prompt change in a simple changelog.

Day 6: Harden for deployment

Add rate limiting, red-team lightweight checks for harmful outputs, and a prompt fallback when the model's confidence is low. Implement simple analytics for usage, latency, and costs.

Day 7: Final user testing and deploy beta

Deploy to a small user group. Hand over artifacts to engineering: prompt file with versions, test cases, data schema, and a replacement plan to move the service to internal APIs if needed.

Concrete prompts used in the project

Below are real-world prompt templates that map to the micro-app lifecycle. Each template is designed to be modular and safe to store in a prompt registry.

1) Normalizer prompt

Assistant task: Normalize the user preference inputs into structured keys: cuisine, budget, distance_km, dietary_tags. Input: {{raw_preferences}}. Output JSON: {"cuisine":..., "budget":..., "distance_km":..., "dietary_tags":[...]}. If info is missing set value to null.

2) Recommendation prompt (system + user)

System: You are a clear, concise dining recommender. Return only valid JSON with keys recommendations, rationale, confidence.
User: Given preferences: {{normalized_prefs}} and available restaurants: {{restaurants_json}}, return up to 5 recommendations ranked by match score. For each recommendation include: name, match_score (0-1), reason short, estimated_walk_time_minutes if location available.

3) Clarifying question prompt

Assistant: If a required field is null in normalized_prefs, ask one focused question to clarify. Example: "Do you prefer fast casual or sit down?" Keep it one sentence only.

4) QA prompt for automated tests

Assistant QA: Given input and expected output, compare the model response to the expected JSON. Return pass/fail and a one-line reason for failure. Use strict JSON equality rules for keys and data types.

Example API call pattern (serverless function)

Below is a minimal pseudocode example for calling a model. Strings use single quotes to avoid JSON escaping issues in the implementation file.

// Pseudocode for serverless handler
const handler = async (req, res) => {
  const prefs = req.body.prefs
  const normalized = await callModel('normalizer_prompt', { raw_preferences: prefs })
  if (needsClarify(normalized)) return res.json({ clarify: true, question: clarifyQuestion(normalized) })
  const restaurants = await fetchAirtableRestaurants(normalized)
  const response = await callModel('recommendation_prompt', { normalized_prefs: normalized, restaurants_json: restaurants })
  res.json(JSON.parse(response))
}

Iteration cadence and version control for prompts

Small apps need disciplined governance to become reusable. Recommended minimal process:

Store prompts in a git repo as plain text with semantic names
Tag prompt versions with release notes stating why you changed them
Maintain a small test suite of 20 input cases that run automatically on each prompt change
Track model, model-version, and temperature used for each prompt run in logs

In 2026 many enterprises have added prompt registries as part of their MLOps flows. If you have access, plug your prompt repo into your registry for auditing and role-based access.

Pitfalls and how to avoid them

Scope creep: Fix a single happy path. Add features only after the MVP validates the hypothesis.
Hallucinations: Use deterministic output formats (JSON) and post-validators. Implement a fact-check step when returning proprietary or safety-sensitive information.
Data privacy: Do not send private user identifiers to generalist assistants. Use on-prem or enterprise model endpoints when handling sensitive data.
Cost surprises: Monitor model calls per session and run a small cost model during prototyping. Prefer lower-cost embeddings and cached retrieval when possible.
Maintenance debt: Version prompts and include a handover document for engineers listing where each prompt maps into production code.

Deployment choices and trade-offs

Choosing the right deployment approach depends on risk tolerance and expected users. Here are common patterns and when to use them.

No-code / low-code (Glide, Bubble, Retool)

Best for fast iteration and user tests. No-code reduces handoff time but can be hard to migrate to a backend later. Good when the app serves under ~500 monthly users and data sensitivity is low.

Serverless functions + static host (Netlify, Vercel)

Balanced approach for teams that expect to scale. Serverless gives you outbound control and secret management. Easier handoff to engineering for productionization.

Internal microservice behind API gateway

Choose this for apps that will be promoted to internal platforms. Build a small REST or gRPC API, put a rate-limit and billing tag on it, and register prompts in a prompt registry for audit trails.

Metrics to instrument from day one

Model latency and 95th percentile latency
Model cost per session
User satisfaction (NPS-style quick thumbs up/down inline)
Prompt version used for the session
Failure rate: sessions that required clarifying questions or manual overrides

Real-world outcomes and learnings

Rebecca's Where2Eat followed a similar path. Key outcomes observed by her and reported in late 2025 were:

Rapid user adoption among close social groups because the app solved one clear pain point
The prompts she developed became the foundation for a small prompt library other non-developer creators reused
When handed to engineers, the documented prompts and test cases reduced rework by more than 50 percent during the first migration sprint

"Vibe coding made it possible for non-developers to create useful personal apps. With a little process and prompt versioning, those apps become valuable artifacts for product teams." — paraphrase of reporting from late 2025

Advanced strategies for teams in 2026

If you are a technology professional or IT admin, consider these advanced tactics to make micro apps a strategic asset.

Prompt library as internal product: Curate, test, and version prompts like code. Expose them via an internal prompts API for product teams to consume.
Model orchestration: Use a small controller to route tasks to the most cost-effective model (e.g., embeddings for search, Claude for complex reasoning) and track lineage.
Automated regression tests: Run your 20-case prompt suite on new model versions to catch behavioral drift. Consider designing the suite like an evaluation pipeline; see practical examples on designing evaluation pipelines.
Security posture: Classify apps by data sensitivity and enforce model endpoints accordingly. Use enterprise-class assistant endpoints for PII or sensitive internal data.

Actionable checklist to run your own 7-day micro-app sprint

Day 0: Define one-sentence problem and three acceptance criteria
Day 1: Write and version a system prompt and two task prompts
Day 2: Build a tiny data model in Airtable or Sheets
Day 3: Wire front end and serverless glue to a model API
Day 4: Test with 5 users and capture 10 failure cases
Day 5: Iterate prompts and add validators
Day 6: Add monitoring, cost checks, and a fallback prompt
Day 7: Deploy to beta and handover artifacts to engineering

Final recommendations

Micro apps are an ideal playground for product discovery. By 2026, the key to turning a prototype into an enterprise asset is not the initial app itself but the artifacts you produce: tested prompts, versioned cases, and a handover document. Focus on the happy path, instrument well, and treat prompts as first-class code.

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