Micro Apps + Fuzzy Search: building a one-week dining recommender like a non-developer

Fix decision fatigue in a week: how a non-developer builds a micro dining recommender with fuzzy search

Hook: You want a tiny, private dining app that understands messy names, reads intent from short messages, and suggests places your friends will like — without hiring an engineer. In 2026 this is easier than you think. This walkthrough recreates a proven micro app workflow and shows how non-developers can assemble a production-like recommender using fuzzy name matching, local embedding search, and prompt templates — with minimal code and hosted SDKs.

Why this matters in 2026

By late 2025 and into 2026, two trends make rapid micro apps practical for non-developers:

• Hosted vector and embedding services (Supabase, Pinecone, and others) made embedding ops one-click and cheap for small apps. (See work using RAG and perceptual AI in production.)
• Low-code UIs and SDKs (Retool, Glide, Make, hosted JavaScript SDKs) let non-developers wire a UI to a backend in hours rather than weeks.

Combine that with accessible fuzzy-matching libraries and compact prompt templates and you can ship a private restaurant recommender in seven days or less.

“Once vibe-coding apps emerged, I started hearing about people with no tech backgrounds successfully building their own apps.” — Rebecca Yu, on building Where2Eat.

What you'll build (the fast summary)

We recreate the micro app workflow in Article 2 — distilled into a practical plan a non-developer can follow:

• Data: a small CSV of restaurants (name, address, cuisine, tags, description)
• Fuzzy name matching: fast local match for typos and partial names (Fuse.js or pg_trgm)
• Embedding search: semantic intent matching for prompts like "I want tacos, budget $" using a hosted vector DB
• LLM prompts: concise templates that explain why each suggestion fits the group's vibe
• UI: a simple no-code or low-code front end (Glide/Retool/Retool-lite) wired to SDK endpoints

Architecture (minimal, practical)

Keep it simple. The micro app uses three capabilities:

1. Exact + fuzzy name lookup for quick, local autocompletes.
2. Vector search over short descriptions/tags for intent matching.
3. LLM to format results and generate friendly explanations.

One-line architecture:

UI (Glide/Retool) --calls--> Hosted endpoints (Supabase Edge Function / Serverless) --uses--> Vector DB (Supabase pgvector or Pinecone) + Fuzzy match lib + LLM embeddings

Components you can choose (non-developer friendly)

• UI: Glide, Bubble, Airtable Interfaces, or Retool
• Vector store: Supabase (pgvector), Pinecone, or Weaviate — pick one with an easy free tier
• Embeddings: hosted APIs (OpenAI, Anthropic, or a managed provider integrated with your vector store)
• Fuzzy name matching: Fuse.js (client-side) or Postgres pg_trgm (server-side)
• LLM: a hosted chat/completion API; use small-context templates for cost control

Step-by-step: build a one-week dining recommender

This plan is tuned for a non-developer who can copy/paste small snippets and use a hosted SDK or low-code tool. It assumes you have a spreadsheet of ~200 restaurants. If you don't, sample datasets are easy to find in open data or local business directories.

Day 0 — Prep your data

1. Create a CSV with columns: id, name, address, cuisine, price_range, tags, description, latitude, longitude.
2. Keep descriptions short (1–2 sentences) — they’re what you’ll embed for semantic search.
3. Example row: La Taqueria, "Authentic tacos, casual, good for groups", tags: taco, cheap, group-friendly.

Day 1 — Import into a hosted DB

Sign up for Supabase (free tier) or Pinecone. Supabase is especially friendly for non-developers because it gives you SQL + pgvector + a GUI.

Import your CSV into a table called restaurants. Add a vector column for embeddings (pgvector) or create a vector index if using a managed vector DB.

Day 2 — Add fuzzy name matching

Two low-friction options:

Client-side: Fuse.js

Use this if your dataset is small (< 5k rows). It runs in the browser and needs no backend changes.

// minimal Fuse.js snippet (client)
const fuse = new Fuse(restaurants, { keys: ['name'], threshold: 0.3 });
const results = fuse.search('latakr'); // handles typos, returns best matches

Server-side: Postgres trigram (pg_trgm)

Use pg_trgm when using Supabase or Postgres. It gives fast fuzzy matching for partial/typoed names and scales better.

-- enable extension once
CREATE EXTENSION IF NOT EXISTS pg_trgm;

-- basic fuzzy query
SELECT *, similarity(name, 'latakr') AS score
FROM restaurants
WHERE name % 'latakr'
ORDER BY score DESC
LIMIT 10;

Why use both? Client-side Fuse.js gives instant UX for autocompletes; server-side pg_trgm gives accurate, authoritative lookups and can be combined with embedding hits.

Day 3 — Generate embeddings for semantic intent

Pick a hosted embedding API. You can do this without writing a model — most vector stores provide connectors or you can call an embeddings API from a no-code workflow.

For each restaurant, build a compact embedding input: combine tags, cuisine, price, and description into a single short text string and embed it.

// pseudocode: Node.js using a hosted SDK
const text = `${name}. ${cuisine}. ${price_range}. ${tags.join(', ')}. ${description}`;
const embedding = await embeddingsClient.create({ input: text });
await vectorStore.upsert({ id, vector: embedding, metadata: { name, cuisine, price_range } });

Most vector stores provide an upsert endpoint. You can do all embedding and upsert in a single script or via a no-code automation tool.

Day 4 — Build the serverless endpoint

Create a tiny endpoint (Supabase Edge Function, Vercel serverless, or a Make webhook) that takes three inputs:

• queryText — what a user types ("tacos for 4 under $30")
• nameCandidate — optional fuzzy name selected by autocomplete
• location/filter — optional area or cuisine filters

Endpoint steps:

1. Run quick fuzzy name lookup (pg_trgm or Fuse) — if a high-confidence match exists, return it early.
2. Otherwise, create an embedding for queryText and run a vector similarity query to get top-k candidates.
3. Combine results: if a fuzzy name hit and vector hits disagree, prefer fuzzy for exact-name needs, else show both ranked.
4. Call the LLM with a prompt template to craft friendly explanations and group-fit reasons.

// pseudocode for endpoint
if (nameCandidate) {
  const fuzzy = runFuzzy(nameCandidate);
  if (fuzzy.score >= 0.6) return formatSingle(fuzzy);
}

const qEmbedding = await embeddingsClient.create({ input: queryText });
const vecHits = await vectorStore.query({ vector: qEmbedding, topK: 5 });
const combined = await rankAndFormat(vecHits);
return combined;

Day 5 — Craft prompt templates (practical and small)

Use short, structured prompt templates that constrain output style and length. Treat the LLM as a formatter and explanation engine, not the source of truth.

// prompt template (system + user)
System: You are a friendly dining assistant. For each candidate restaurant, produce one-line reason why it fits the party.

User: Here are candidate restaurants with metadata. For each, return: name — short reason (15–30 words) — top tags.

Data:
1) {name} | cuisine: {cuisine} | price: {price} | tags: {tags} | notes: {description}

Return JSON array.

This keeps the LLM's job simple: explain alignment between query intent and restaurant attributes. That reduces hallucinations and cost.

Day 6 — Wire the UI

Use Glide or Retool to build a lightweight UI with three elements:

• Search box with Fuse.js autocompletes
• Quick filter buttons (cuisine, price)
• Results with LLM explanations and a “Why this?” expand toggle

Glide can call your serverless endpoints via REST. Retool can connect directly to Supabase and your functions, letting you preview behavior without code. Keep prompts externalized (see notes on modular templates) so non-developers can tweak tone and safety.

Day 7 — Polish and test with friends

• Invite your group, ask them to search with intentional typos and vague intents ("cheap tacos for 3").
• Track false negatives: categorize where the app didn’t find a result and add missing tags/descriptions.
• Optimize latency: move embeddings to cache, increase vector index replicas if needed (for small apps, the free tiers usually suffice). See field notes on portable networking if you hit bandwidth issues in remote tests.

Examples: combining fuzzy match + embeddings + prompts

Here are concrete snippets showing how the pieces interact in the endpoint.

1) Quick fuzzy check (pg_trgm SQL)

SELECT id, name, similarity(name, $1) AS score
FROM restaurants
WHERE name % $1
ORDER BY score DESC
LIMIT 5;

2) Vector similarity (Supabase-style SQL)

SELECT id, name, vector_cosine_distance(embedding, $1) AS distance
FROM restaurants
ORDER BY distance ASC
LIMIT 10;

3) Minimal LLM prompt (JSON output)

System: You are a concise assistant.
User: Given these restaurants and the request "tacos for 4 under $30", return JSON: [{name, reason, matched_tags}]

Data:
... (restaurant metadata)

Return value example:

[
  {"name":"La Taqueria","reason":"Authentic tacos, large portions and group-friendly seating; fits a $-friendly budget","matched_tags":["taco","cheap","group-friendly"]},
  {"name":"Taco Hub","reason":"Late-night tacos and combos, good for casual groups","matched_tags":["taco","late-night"]}
]

Operational tips and tradeoffs

Small micro apps don't need high-cost solutions, but you should know tradeoffs:

• Latency: Client-side fuzzy gives instant feedback; vector queries add ~50–200ms depending on provider. Cache repeated queries.
• Cost: Keep embedding calls to a minimum. Embed your static dataset once and only embed user queries. Use small embedding models and control token count — this aligns with best practices from cloud cost optimization.
• Accuracy: Combine fuzzy and embedding results. Fuzzy is great for names; embeddings for intent and partial descriptions.
• Scaling: For a personal micro app, use free tiers. If your app grows, migrate to an indexed vector DB and use batching for embeddings.

Real-world validation and 2026 trends

Micro apps like Where2Eat proved the pattern: a one-person project can deliver huge UX wins. In 2026 we've seen:

• More accessible SDKs for embeddings and vectors; providers now offer both REST and no-code connectors that make ingestion trivial.
• New lightweight client LLMs for formatting on-device, which reduces API costs for simple text formatting tasks — see work on on-device interfaces.
• Standards for prompt templates and intent labels that help non-developers reuse recipes between micro apps.

Common pitfalls and fixes

Pitfall: Over-reliance on LLMs for search

Fix: Use LLMs only for explanations and UX polish. The search and retrieval layer should be deterministic (fuzzy and vector). This reduces hallucinations; observability practices from observability for microservices are useful when tracking mismatches between search and LLM outputs.

Pitfall: Too many embedding calls

Fix: Embed static rows once. Cache query embeddings where possible. For repeated queries, return cached responses.

Pitfall: Poor autocomplete UX

Fix: Combine client-side Fuse.js for instant suggestions with server validation using pg_trgm. Show “Did you mean…” only when high confidence.

Checklist: launch in seven days

• Day 0: CSV with 100–500 restaurants
• Day 1: Import to Supabase/Pinecone
• Day 2: Add fuzzy autocompletes (Fuse.js or pg_trgm)
• Day 3: Generate and upsert embeddings
• Day 4: Serverless endpoint for search + ranking
• Day 5: Prompt templates and LLM integration
• Day 6: Low-code UI wiring
• Day 7: Invite users, collect feedback, iterate (use a short weekly planning template for the seven-day sprint)

Actionable takeaways

• Combine fuzzy name matching and vector search — they solve different problems.
• Minimize LLM scope: use it for explanations and UX copy, not as the primary search mechanism.
• Use hosted SDKs: Supabase, Pinecone, or managed embedding providers let non-developers move fast.
• Test with typos and vague intent: those tests are the most valuable UX feedback.

Final notes and future-proofing

In 2026, micro apps are increasingly practical and temporary by design. Build with modular pieces so you can replace providers without re-architecting. Keep prompts externalized in a small template file so non-developers can tweak tone and safety without touching code. If you need visual editing for templates or docs, consider tools like Compose.page to let non-developers edit prompts and examples safely.

Call to action

If you want a starter repository or a short script to import your CSV into Supabase and seed embeddings + fuzzy indexes, try the free Supabase tier and a 30-minute walkthrough using the templates above. Start your seven-day build today — ship a micro dining recommender that finally ends dinner decision fatigue for your group.

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