How to Build a Restaurant Recommendation Micro App Using Claude or ChatGPT

Build a Restaurant Recommendation Micro App with Claude or ChatGPT — Fast, Practical, and Production-Ready

Decision fatigue, endless group-chat threads, and a desire to ship quickly: if that sounds familiar, you’re the developer this guide is for. Inspired by Rebecca Yu’s week-long “vibe-code” dining app and the 2025–26 surge in AI-driven micro apps, this walkthrough shows how to design the data model, craft high-impact prompts, add per-user personalization, deploy in days, and apply governance for safe, maintainable micro apps using Claude or ChatGPT.

Why a micro app — and why now (2026 context)

Micro apps (aka personal or fleeting apps) are booming because AI and low-friction hosting let developers and non-developers ship useful tooling fast. Late 2025 and early 2026 saw two big trends that matter for this project:

• Anthropic’s tools (Claude, Claude Code, and the Cowork preview) pushed desktop and agent workflows into production scenarios — making local, file-system-aware assistants viable for rapid prototyping.
• OpenAI and major cloud vendors expanded vector DB integrations and cheaper embeddings, enabling Retrieval-Augmented Generation (RAG) and personalized recommendations at lower cost.

That means you can build a responsive, private restaurant recommendation micro app in days — with the same core UX patterns used by larger products.

Project scope: What this micro app will do

In this walkthrough we’ll build a web micro app that:

• Stores a small catalog of restaurants (menu highlights, cuisine tags, location, price level, open hours).
• Creates per-user profiles and group-vibe sessions (so friends can vote or veto).
• Uses Claude or ChatGPT for natural language reasoning, ranking, and conversational clarification.
• Supports personalization using lightweight embeddings and a vector DB for similarity.
• Deploys as a serverless web app with managed vector storage and observability.

Step 1 — Design your data model (minimal and realistic)

Keep the model intentionally small: a few normalized tables plus a document store for unstructured info. This keeps development fast and reduces cost.

Core entities

• restaurants: canonical list
• users: profile, preferences
• sessions: group vibe sessions
• votes: user votes per session
• embeddings: vector references for personalization

Example Postgres schema (simplified)

CREATE TABLE restaurants (
  id UUID PRIMARY KEY,
  name TEXT NOT NULL,
  address TEXT,
  cuisine TEXT[],
  price_level INT, -- 1..4
  rating FLOAT,
  short_desc TEXT,
  tags TEXT[],
  hours JSONB,
  created_at TIMESTAMPTZ DEFAULT NOW()
);

CREATE TABLE users (
  id UUID PRIMARY KEY,
  name TEXT,
  email TEXT UNIQUE,
  preferences JSONB, -- e.g. {"likes":"sushi","diet":"vegan"}
  embedding_ref TEXT -- pointer to vector DB
);

CREATE TABLE sessions (
  id UUID PRIMARY KEY,
  owner_id UUID REFERENCES users(id),
  name TEXT,
  context JSONB,
  created_at TIMESTAMPTZ DEFAULT NOW()
);

CREATE TABLE votes (
  id UUID PRIMARY KEY,
  session_id UUID REFERENCES sessions(id),
  user_id UUID REFERENCES users(id),
  restaurant_id UUID REFERENCES restaurants(id),
  vote INT -- score, 0..10
);

Store verbose descriptions and menus as JSONB or in a small document store to power RAG. Keep your restaurant catalog manageable (100–1,000 rows) for a true micro app.

Step 2 — Choose your AI architecture: Claude vs ChatGPT

Both Claude and ChatGPT are capable. Your choice depends on the workflow you want and licensing/ops constraints.

Claude (Anthropic)

• Good for long-form reasoning and constrained safety policies (Anthropic’s constitutional approach).
• Claude Code/Cowork bring agentic capabilities useful for local prototyping and file access.
• Prefer Claude if you need stronger built-in guardrails or plan to use desktop agent features in early prototypes.

ChatGPT (OpenAI)

• Broad ecosystem: plugins, fine-tuning (historically), and more third-party integrations.
• Often has faster latency in managed endpoints and extensive community examples.
• Prefer ChatGPT if you need plugin-style external calls or easier integration with widely used SDKs.

In practice you can design a provider-agnostic AI layer supported by an adapter pattern so you can swap Claude and ChatGPT without changing the rest of the app.

Step 3 — Prompt engineering and control flow

Prompt engineering is where you convert developer intent into reliable outputs. For a restaurant recommender, you need three prompt roles:

1. Clarifier: Ask follow-ups when user input is ambiguous.
2. Ranking engine: Score restaurants by relevance to user or group context.
3. Explanations/Responses: Return short, actionable justification for recommendations.

Example prompt template (ranking)

System: You are an assistant that ranks restaurants by fit for a group.
Assistant Task: Given user_profile, group_preferences, and a list of candidate_restaurants, return the top 5 restaurants in JSON with fields: id, score (0-100), reason (one sentence).
Context: {user_profile}
Group: {group_context}
Candidates: {candidates_json}
Return: JSON only.

Key engineering tips:

• Use a strict system instruction to limit hallucination and force structured JSON.
• Return scores normalized to a fixed range (0–100) to make downstream filtering deterministic.
• Use few-shot examples in the prompt for edge cases (e.g., dietary restrictions).
• When using Claude, prefer slightly longer, reasoning-focused system instructions; with ChatGPT, short and explicit system messages plus examples work well.

Step 4 — Personalization: embeddings, vector DB, and lightweight profiles

Personalization doesn’t require massive models or custom training. Use embeddings + similarity and minimal context windows to personalize results.

Workflow

1. Generate embeddings for restaurant descriptions and for user preferences (stored on profile creation or update).
2. Store vectors in a managed vector DB: Pinecone, Redis Vector, Milvus, or Weaviate are all viable in 2026.
3. On query, perform a similarity search to fetch top N candidates, then pass those to the LLM ranking prompt for final scoring.

Why RAG + embeddings?

It balances cost and quality: embeddings filter the search space (cheap), and the LLM performs higher-cost reasoning only on a small candidate set. This pattern is now mainstream in 2026 and is supported by both OpenAI and Anthropic ecosystems.

Step 5 — Rapid prototyping: stack and deployment options

For a rapid micro app you want low-friction hosting, serverless functions for AI calls, and easy CI/CD. Here are recommended stacks:

Frontend

• React (Next.js) or SvelteKit — Next.js is still the default for app + edge functions.
• Tailwind for quick UI and consistent UX.

Backend

• Serverless API routes (Vercel / Netlify / Cloudflare Pages + Functions) to handle AI calls, RAG, and auth.
• Postgres (Neon, Supabase) for relational data; choose Neon for fast branching dev workflows.
• Vector DB: Pinecone for simplicity, RedisVector for low-latency single-provider setups, or a cloud-managed Milvus for scale.

Deployment pattern

1. Local dev with environment variables for API keys.
2. Deploy to Vercel or Cloudflare Pages for immediate preview URLs.
3. Use feature branches and ephemeral previews to share the app with friends (micro app workflow).

Tip: Use a provider that supports edge functions if you need latency under 100ms for simple routing; otherwise conventional serverless is fine.

Step 6 — Security, governance, and costs

Micro apps can turn into production surprises if you don’t add minimal governance. Treat this as a first-class concern.

Security checklist

• Store AI keys in secure secrets manager; never expose them client-side.
• Use rate limits on API routes to prevent runaway costs and abuse.
• Sanitize user-submitted data and validate JSON from LLMs before persisting.
• Apply CORS and auth; even personal micro apps should support OAuth or magic links to avoid open endpoints.

Governance

Implement these small but effective governance controls:

• Response validation schema (reject non-JSON or off-spec outputs).
• Log LLM prompts and responses in a siloed audit log for debugging and safety reviews.
• Apply content filters or safety heuristics (especially for user-generated prompts) — both Anthropic and OpenAI provide moderate safety tooling in 2026.

Cost controls

• Cache frequent recommendations for a session to avoid repeated LLM calls.
• Use embeddings + similarity to reduce the number of tokens sent to the LLM.
• Use token or request budgets per user / session and send alerts when thresholds are approached.

Step 7 — UX patterns for recommendations and group decision-making

Small UX decisions have oversized effects on quality of experience.

Vibe session flow

1. User initiates session and shares link with friends.
2. Each participant selects top preferences (quick toggles: cuisine, price, distance).
3. LLM clarifies ambiguous preferences (e.g., "Do you mean Japanese or fusion?").
4. System returns top 5 ranked restaurants with short reasons and a map link.
5. Participants vote; LLM re-ranks based on votes and returns final shortlist.

Design patterns

• Keep recommendations short: 1–2 line reasons (helps trust and reduces hallucination risk).
• Show provenance tags ("matched on: vegan options, 0.8 similarity to your preferences").
• Enable quick veto: a single "not for me" action that updates the session context and re-runs ranking.

Step 8 — Example integration flow (high-level)

Here’s a concise flow for an incoming request to recommend restaurants:

1. Frontend sends session context + user id to your serverless API.
2. API fetches user embedding, runs vector similarity to get top 20 restaurants.
3. API constructs ranking prompt with candidate JSON and sends to LLM (Claude or ChatGPT).
4. LLM returns a ranked JSON — API validates, caches, and returns to client.
5. Client displays results with provenance, map links, and vote buttons.

Step 9 — Quality checks and observability

For a micro app that may quickly grow beyond personal use, add light observability:

• Collect metrics: LLM call latency, tokens per call, cache hit rates, vote conversion.
• Error tracking: Sentry or an equivalent to capture API and prompt failures.
• Regular audits of LLM outputs for safety and hallucination trends — keep a small human review loop.

Advanced strategies and future-proofing (2026+)

As of 2026 there are new capabilities to prepare for:

• Local agent previews: Tools like Claude Cowork let you prototype agents that access local files — great if you want integrations like calendar-aware recommendations.
• Multimodal signals: Image embeddings and geospatial-aware vectors can improve local restaurant matching (menus, photos, seating maps).
• Composable LLMs: Chain model calls (clarifier → retriever → ranker) and use cheaper specialized models for sub-tasks to cut costs.

Shortcoming and trade-offs

No micro app is without trade-offs. Expect these realities:

• LLM responses can drift; add validation and retuning cycles.
• Embedding costs and vector DB storage are ongoing — but manageable for a small catalog.
• Privacy matters more as user count grows; plan for encryption and data deletion policies early.

"Vibe coding" is about speed and iteration — but fast prototypes must still ship with guardrails. Treat AI outputs as suggestions, not ground truth.

Example prompt bank (copy-paste-ready)

Clarifier (multi-turn)

System: You are a concise assistant. When the user's preference is ambiguous ask a single clarifying question.
User: "I want something casual tonight"
Assistant (expected): "Do you mean casual ambience (e.g., pubs/cafes) or casual price (under $30 per person)?"

Ranker (JSON output)

System: Rank candidates by fit and return JSON: [{"id":...,"score":0-100,"reason":"one sentence"}].
Input: {user_profile}
Candidates: {candidates}

Use the ranker for deterministic outputs and the clarifier to clean up ambiguous input before ranking.

Real-world checklist — ship in days

1. Choose stack and register API keys (Anthropic/OpenAI, vector DB, Postgres).
2. Model your schema and seed 50–200 restaurants.
3. Implement embedding generation for restaurants and users.
4. Build serverless endpoint for ranking with strict validation.
5. Create a minimal frontend with session flow and voting UI.
6. Deploy to Vercel/Cloudflare and run user tests with friends.
7. Add rate limits, logs, and a token budget per user.

Conclusion — ship fast, iterate safely

Rebecca Yu’s Where2Eat shows that micro apps can be built with focus and intent. In 2026 the tools (Claude Cowork, Claude Code, improved ChatGPT integrations, cheaper embeddings) make it even easier to prototype and deploy a personal restaurant recommendation micro app in days. Prioritize a tight data model, an embeddings-first personalization layer, clear prompt contracts, and minimal governance to keep costs and risk in check.

Start small, measure, and iterate — and treat the LLM as a reasoning layer, not an authoritative data source. That approach will keep your micro app both useful and trustworthy.

Actionable next steps (today)

• Seed a Postgres table with 50 restaurants and generate embeddings for each.
• Implement a serverless ranking endpoint using the prompt templates above.
• Deploy a Next.js preview and invite 3 friends to test a vibe session.

If you want a starter repo, example prompts tuned for Claude and ChatGPT, or a short checklist for governance and cost controls, sign up for our developer kit and get a downloadable project scaffold you can run in under an hour.

Call to action

Ready to build your own restaurant recommendation micro app? Grab the starter repo, tailored prompt bank, and deployment checklist from webtechnoworld.com — or email our team to run a 2-hour workshop to get you from idea to deployed micro app in a day. Ship faster, iterate safer.

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