Build a Restaurant Recommender Micro-App Using Tasking.Space and OpenAI/Claude

Stop juggling chat threads and spreadsheets — build a tiny recommender that consolidates preferences in Tasking.Space, calls an LLM (OpenAI or Claude) to score options, and returns ranked restaurant choices.

If your team is tired of decision friction — long Slack threads, five competing Google Sheets, and one person acting as the de facto restaurant oracle — a focused micro-app can remove the friction in a single sprint. In 2026 the micro-app trend (aka vibe-coding) accelerated: non-developers ship functional apps using AI and no more than a few APIs. This tutorial walks you through a hands-on, production-minded pattern: collect preferences in Tasking.Space, trigger a webhook, call an LLM (OpenAI or Claude) to score candidate restaurants, and return a ranked list into Tasking.Space. You’ll get ready-to-run code, prompt examples, scoring rubrics, and crucial rate-limit strategies for both OpenAI and Anthropic Claude.

Why this pattern matters in 2026

By late 2025 and into 2026 we saw two important shifts that make this pattern especially useful:

• Anthropic and OpenAI scaled developer-grade models and desktop agent tooling (Anthropic’s Cowork preview is an example), making low-latency scoring and agent workflows easier for micro-apps.
• Teams adopted micro-apps to automate specific decision flows like meeting room booking, on-call routing, and place recommendations — reducing context switching and improving throughput.

“Vibe coding” and personal micro-apps turned casual builders into productivity architects; a simple recommender can save tens of minutes per decision per week.

High-level flow

1. User completes a Tasking.Space form (preferences: cuisine, price, distance radius, dietary needs, party size, vibe).
2. Tasking.Space fires a webhook to your micro-app server.
3. Your server collects candidate restaurants (from internal DB, Places API, or Tasking.Space knowledge store) and builds a compact scoring prompt.
4. Your server calls an LLM (OpenAI or Claude) to score and rank candidates in a strict JSON format.
5. Your server posts the ranked list back to Tasking.Space (update task comments, create child tasks, or set task status) and notifies users.

Core principles before you code

• Design for deterministic output: require JSON with a fixed schema to avoid hallucinations; consider explainability and versioned prompt schemas.
• Batch where possible: one prompt to score N options is almost always cheaper and easier to rate-limit than N separate calls. Combine this with cache-first strategies.
• Fail fast, then degrade gracefully: if the LLM is rate-limited, return a cached fallback or a simple heuristic ranking.
• Instrument every step: log latency, request IDs, prompt versions, and LLM model used for reproducibility and A/B testing. Consider technical signal best practices from instrumentation playbooks.

Example data model

Keep your payloads compact. Here’s a minimal JSON spec you’ll pass to the LLM and expect back:

{
  "preferences": {
    "cuisine": ["Japanese", "Korean"],
    "max_price": 2,          // 0-4 price scale
    "radius_meters": 3000,
    "dietary_restrictions": ["vegetarian"],
    "vibe": "casual",
    "party_size": 4
  },
  "candidates": [
    {"id":"r1","name":"Sushi Corner","rating":4.6,"distance_m":1200,"price":2,"tags":["sushi","counter"]},
    {"id":"r2","name":"BBQ House","rating":4.2,"distance_m":800,"price":1,"tags":["korean","shared-plates"]}
  ]
}

Prompt engineering: exactness wins

Two ways to reduce hallucinations and ensure consistent parsing:

1. Ask the model to only output JSON with a strict schema and nothing else.
2. Provide a scoring rubric and request numeric scores (0–100) for each candidate on key dimensions, then compute an aggregate weighted score.

Scoring rubric (example)

• Relevance to cuisine (weight 35%)
• Dietary fit (weight 25%)
• Distance and logistics (weight 15%)
• Price fit (weight 15%)
• Vibe match (weight 10%)

OpenAI-style prompt (chat completion / function calling)

System: You are a strict scoring agent. Output EXACTLY a JSON array called "ranked".
Include for each candidate: id, name, scores {relevance, dietary, distance, price, vibe}, aggregate_score (0-100).
Weighting: relevance 35, dietary 25, distance 15, price 15, vibe 10.
Do NOT include any explanatory text.

User: Here are preferences and candidates: 

Return only the JSON object.

Claude-style prompt

Anthropic models respond best to clear instructions and a pattern for output. Use explicit markers and request a JSON-only response. Example instruction:

You are Claude. Evaluate each candidate against the provided preferences.
Return only JSON matching this schema: { "ranked": [{"id": string, "name": string, "scores": {"relevance": int, "dietary": int, "distance": int, "price": int, "vibe": int}, "aggregate_score": int }] }

Weighting: relevance 35, dietary 25, distance 15, price 15, vibe 10.

Server code: webhook receiver + LLM call (Node.js example)

This minimal Express server handles a Tasking.Space webhook, collects candidates, calls the LLM (batched), and updates Tasking.Space. Replace placeholders with your keys and endpoints.

const express = require('express');
const fetch = require('node-fetch');
const app = express();
app.use(express.json());

const TASKING_API_KEY = process.env.TASKING_API_KEY;
const OPENAI_KEY = process.env.OPENAI_API_KEY; // or CLAUDE_KEY

// Verify signature (Tasking.Space should sign webhooks) - pseudocode
function verifySignature(req) {
  // Implement HMAC check with TASKING_WEBHOOK_SECRET
  return true;
}

app.post('/webhook/preferences', async (req, res) => {
  if (!verifySignature(req)) return res.status(401).end();
  const payload = req.body; // contains task id and preferences
  const taskId = payload.task_id;
  const preferences = payload.preferences;

  // 1) Fetch candidates (could be a Places API or your DB)
  const candidates = await fetchCandidates(preferences);

  // 2) Build compact prompt
  const promptPayload = { preferences, candidates };

  // 3) Call the LLM (batch)
  const llmResponse = await callOpenAI(promptPayload);
  if (!llmResponse) {
    // fallback: simple heuristic ranking
    const fallback = candidates.sort((a,b)=>b.rating-a.rating).slice(0,5);
    await updateTaskWithResults(taskId, fallback);
    return res.status(200).json({ status: 'fallback' });
  }

  // 4) Post results back to Tasking.Space
  await updateTaskWithResults(taskId, llmResponse.ranked);
  res.status(200).json({ status: 'ok' });
});

async function callOpenAI(payload) {
  const messages = [
    { role: 'system', content: 'You are a JSON-only scoring agent...' },
    { role: 'user', content: JSON.stringify(payload) }
  ];
  const r = await fetch('https://api.openai.com/v1/chat/completions', {
    method: 'POST',
    headers: { 'Authorization': `Bearer ${OPENAI_KEY}`, 'Content-Type': 'application/json' },
    body: JSON.stringify({ model: 'gpt-4o-mini', messages, temperature: 0, max_tokens: 800 })
  });
  if (!r.ok) return null;
  const data = await r.json();
  const text = data.choices[0].message.content;
  try { return JSON.parse(text); } catch (e) { return null; }
}

async function updateTaskWithResults(taskId, ranked) {
  const body = { comments: formatRankedMarkdown(ranked) };
  await fetch(`https://api.tasking.space/v1/tasks/${taskId}`, {
    method: 'PATCH',
    headers: { 'Authorization': `Bearer ${TASKING_API_KEY}`, 'Content-Type': 'application/json' },
    body: JSON.stringify(body)
  });
}

app.listen(3000);

Claude call example (Python)

import requests, os, json
CLAUDE_KEY = os.environ['CLAUDE_KEY']

prompt = 'You are a JSON-only scoring agent...' + json.dumps(payload)
resp = requests.post('https://api.anthropic.com/v1/complete',
    headers={'x-api-key': CLAUDE_KEY},
    json={'model': 'claude-2.1', 'prompt': prompt, 'max_tokens_to_sample': 800, 'temperature': 0})
if resp.ok:
    text = resp.json()['completion']
    result = json.loads(text)

Note: Anthropic/Claude APIs and model names evolve quickly. Use the latest client libraries and check rate-limit docs (Anthropic and OpenAI both publish limits per account).

Best practices for outputs and reliability

• Require strict JSON with a top-level key like "ranked" — it prevents extraneous text from breaking parsing.
• Validate the schema server-side after parsing: type checks, bounds (0–100), and presence checks.
• Enforce temperature=0 for deterministic scoring. Use higher temperature only in creative recommendation modes.
• Log prompt versions so you can A/B test scoring weights and text phrasing.

Rate-limiting and cost strategy

LLM calls are the primary cost and latency factor. Here are practical patterns to handle limits and keep latency predictable.

1) Batch scoring

Send all candidates in one prompt and ask the model to return a ranked list. This reduces round-trips and tokens versus N separate calls. Pair batching with cache-first responses for common queries.

2) Debounce and coalesce requests

If multiple users edit preferences concurrently, debounce incoming webhook events for a few hundred milliseconds and coalesce them into one scoring job.

3) Concurrency limits and queueing

Use a job queue (BullMQ, Sidekiq, Celery) with a concurrency cap that matches your LLM rate limits. Implement token-based throttling if your cost model charges per-token.

4) Exponential backoff + jitter

On 429s, back off with exponential delays and random jitter. Respect Retry-After headers when present.

5) Cache frequent queries

If the same preference set and candidate list appear repeatedly (common for “lunch group A”), cache the ranked result for a TTL (e.g., 15–60 minutes). This saves costs and improves responsiveness.

6) Fallback heuristics

Always provide a non-LLM fallback: sort by rating and proximity or use a lightweight scoring function. Users prefer a somewhat-correct answer now over a perfect answer later.

Error handling and security

• Webhook signature verification: verify HMAC signatures on Tasking.Space webhooks before trusting inputs.
• Rate-limit your downstream requests: do not retry unlimited times; cap at 3 attempts.
• API keys and secrets: rotate keys, store them in secrets managers, and avoid printing them to logs.
• Data minimization: strip PII from prompts unless necessary; use hashed IDs instead of user names when feasible.

Testing and evaluation

To measure effectiveness and avoid model drift:

• Track CTR or acceptance: did users pick one of the top-3 suggestions?
• Collect feedback: add a single-click thumbs-up/thumbs-down to the Tasking.Space result comment to gather labels for prompt tuning.
• Run periodic prompt audits: check that JSON output continues to match schema after model upgrades.

Real-world mini-case: "Where2Eat" micro-app, reimagined

Rebecca Yu’s week-long Where2Eat prototype is emblematic of the micro-app wave: fast to build, focused on one decision, and used by a small group. For a team at a 100-person startup, the same pattern scaled: preferences stored as tasks in Tasking.Space; a micro-app valued at saving 15 minutes per team lunch decision multiplied by dozens of weekly decisions per team; aggregated outcome: fewer interruptions and happier engineering teams.

Advanced strategies and future-proofing (2026+)

• Model orchestration: use a small LLM for quick heuristics and switch to a larger one for edge cases or explanations.
• Hybrid retrieval: combine vector search of past group choices or menu extraction with the LLM’s scoring to ground recommendations.
• Agentic follow-ups: integrate Claude/OpenAI agent primitives (available in late-2025 tools) to autonomously call external APIs (reservations) when confirmed by a human.
• On-device inference: for privacy-focused deployments, evaluate on-device models for initial filtering and cloud LLMs for final scoring.

Prompt variants: A/B tests to try

1. Weight emphasis on dietary constraints vs. vibe to observe acceptance rates.
   • Prompt A: dietary first (strict filter). Prompt B: dietary scored but non-blocking.
2. Ask the model for reasons only when a user taps "Why this?" — reduces token usage.
3. Test ordinal vs. absolute scoring output. Some models produce more stable numeric scores when asked for 0–10 instead of 0–100.

Checklist before you ship

• Webhook verification implemented and tested
• Batch scoring implemented and validated
• JSON schema enforced with tests
• Rate-limit and retry policy in place
• Cache and fallback heuristics configured
• Logging for prompt, model, latency, and result IDs

Actionable takeaways

• Batch first: one prompt to score multiple restaurants reduces cost and rate-limit pressure.
• JSON-only outputs: make parsing deterministic and safe.
• Always have a fallback: cached or heuristic results keep UX snappy during outages or rate limits.
• Measure: log acceptance rates to iterate on prompt weights and UX.

Where to go next

Start small: wire a Tasking.Space form and webhook to a serverless function that returns a cached ranked list. Once that works, add an LLM scoring step with strict JSON output. Keep experiments short — tune weighting and prompt variants based on real usage data, not intuition.

Call to action

Ready to ship your own restaurant recommender micro-app? Clone the starter repo, deploy the webhook to your cloud provider, and connect Tasking.Space. Instrument the first 100 requests, then iterate: change prompt weights, add A/B tests, and roll out to other decision flows (meeting rooms, equipment pickups, on-call handoffs). If you want a proven scaffold, sign up for Tasking.Space, check the API docs for webhook and task update endpoints, and try both OpenAI and Claude with the JSON-only prompt pattern. Build once, automate forever — your team will thank you.

Related Reading

• Building and Hosting Micro‑Apps: A Pragmatic DevOps Playbook
• Edge AI Code Assistants in 2026: Observability, Privacy, and the New Developer Workflow
• How On-Device AI Is Reshaping Data Visualization for Field Teams in 2026
• Edge-Powered, Cache-First PWAs for Resilient Developer Tools — Advanced Strategies for 2026
• Politics, Policy, and Your Health Care: How Culture Wars Affect Coverage and Access
• Framing & Display for High-Value Art — How to Treat Prints, Originals and Heirlooms
• Podcast Distribution via BitTorrent: Lessons from Mainstream Podcasters Entering the Space
• How Gmail’s New AI Changes Should Change Your Award Announcement Emails
• How to Claim Telecom Outage Credits (and When to Push for More): A Simple Template