Prompt Library: Templates for Building Micro-Apps (Discovery, Recommendation, Workflow)

Ship a micro-app in days: a curated prompt library for non-developers

Decision fatigue, slow integrations, and engineering backlogs are the top blockers for teams that want small, production-ready automation and conversational micro-apps. In 2026 the fastest route isn’t writing full-stack code — it’s assembling prompts, data mappings and no-code connectors into repeatable micro-app patterns: discovery, recommendation and workflow. This article gives a ready-to-use prompt library, tested patterns for Claude and ChatGPT, and step-by-step instructions non-developers can follow to build micro-apps that actually deliver value.

Why micro-app prompts matter now (2026 context)

Micro-apps — personal or team-targeted single-purpose apps — exploded in late 2024–2025 and matured in 2026. Tools like Anthropic’s Cowork and Claude Code blurred the line between automation and desktop AI, and major LLMs optimized for tools and agents made vibe-coding accessible to non-engineers. The result: business teams expect quick, reliable micro-apps for recommendations, scheduling and workflows without 6–8 week engineering sprints.

“Once vibe-coding apps emerged, I started hearing about people with no tech backgrounds successfully building their own apps.” — Rebecca Yu, creator of Where2Eat (2024–2025 micro-app example)

How to use this prompt library (practical checklist)

1. Pick a micro-app type: Recommendation, Scheduling, or Workflow.
2. Choose an execution path: Chat UI (ChatGPT/Claude), No-code platform (Make/Make.ai, Zapier, Retool, Glide), or Hybrid (API + webhook).
3. Adapt the template to your data fields (user preferences, calendar IDs, CRM fields).
4. Set output schema so the no-code tool can parse responses (JSON preferred).
5. Test iteratively with sample user inputs, adjust temperature/constraints, and build validation rules.
6. Instrument and monitor performance and user satisfaction (NPS, conversion, task completion).

Core prompt engineering rules for non-developers

• Always supply a system/instruction layer: Explicitly tell the model its role and response format.
• Use constrained outputs: JSON or CSV so connectors can parse automatically.
• Give edge-case examples: Add 2–3 few-shot examples to teach behavior for ambiguous inputs.
• Set temperature low for deterministic tasks: 0–0.3 for scheduling/recommendation ranking.
• Validate responses: Add a quick validation step in your no-code flow to check required fields and ranges.

Prompt templates — discovery, recommendation and workflow

Below are production-ready prompt templates. Each template includes:

• System prompt (role + constraints)
• User prompt (what your end user types)
• Expected machine output (JSON schema)
• Implementation notes for no-code connectors

1) Discovery prompt — gather user preferences quickly

Use discovery prompts when you need structured preferences from a user before making recommendations or scheduling.

You are a concise data collection assistant. Ask follow-up questions to collect the following fields: topic, priority (low/medium/high), timeframe (ASAP/today/week/month), constraints (budget, attendees, location), and 3 optional tags. Return a single JSON object with exactly those keys. If any field is unknown, set it to null.

I'm planning a team lunch next week. Help me capture preferences.

{
  "topic": "team lunch",
  "priority": "medium",
  "timeframe": "week",
  "constraints": {"budget": "£20 per person", "attendees": 8, "location": "London"},
  "tags": ["casual", "central", "vegetarian-friendly"]
}

Implementation notes:

• Hook this prompt to a chat widget or form. If values are null, the next step should ask only the missing fields.
• Store result in Airtable/Notion to feed recommendation prompts.

2) Recommendation prompt — ranked, explainable suggestions

Recommendation prompts should return a ranked list plus a short rationale and an action token (e.g., add-to-calendar link or choice ID).

You are an expert recommendation engine. Given user preferences and a small dataset (max 50 items), return a JSON array of up to 5 ranked candidates. Each candidate must include: id, name, score (0-100), top_reasons (array), and action (object with type and payload). Keep output concise and deterministic. Use a neutral tone.

Preferences: {"priority":"medium","timeframe":"week","constraints":{"budget":"£20","location":"Soho"}}
Dataset: [
  {"id":"r1","name":"Bella Trattoria","price":"£15","distance_km":1.2,"tags":["italian","cozy","vegetarian"]},
  {"id":"r2","name":"Noodle House","price":"£10","distance_km":0.8,"tags":["asian","fast","vegan"]}
]
Return recommendations.

[
  {"id":"r2","name":"Noodle House","score":92,"top_reasons":["fits budget","closest to location","vegan options"],"action":{"type":"open_url","payload":"https://booking.example/r2"}},
  {"id":"r1","name":"Bella Trattoria","score":78,"top_reasons":["price within budget","vegetarian options","cozy for groups"],"action":{"type":"open_url","payload":"https://booking.example/r1"}}
]

Implementation notes:

• Import dataset from Airtable or a Google Sheet. Keep the list small for cost control or use vector search for larger catalogs.
• Attach the JSON output to a no-code UI: show top 3 in the chat, with action buttons for each action.payload.
• Set temperature = 0–0.2 for consistent ranking.

3) Scheduling workflow prompt — find and confirm meeting slots

This template handles availability checks, proposing slots and returning machine-readable calendar actions.

You are an assistant that proposes calendar slots. Input: user timezone, required duration (minutes), attendee emails, and a calendar-free/busy API response. Output: a JSON object with proposed_slots (array), each slot has start_iso, end_iso, score (0-100), and reason. If no slot is found, propose a fallback: "Suggest 3 next-best dates". Keep timezone conversions precise.

Timezone: Europe/London
Duration: 60
Attendees: ["alice@example.com","bob@example.com"]
Calendars free_busy: {"alice": [/* busy ranges */], "bob": [/* busy ranges */]}
Return 3 proposed slots this week.

{
  "proposed_slots": [
    {"start_iso":"2026-01-21T11:00:00+00:00","end_iso":"2026-01-21T12:00:00+00:00","score":95,"reason":"All attendees free; within preferred hours"},
    {"start_iso":"2026-01-22T14:00:00+00:00","end_iso":"2026-01-22T15:00:00+00:00","score":82,"reason":"One attendee with low-conflict window"}
  ],
  "fallback":"Suggest 3 next-best dates in the following week"
}

Implementation notes:

• Use Calendar APIs (Google, Microsoft) via no-code connectors. Convert free/busy to the API schema the prompt expects.
• After selection, call a create-event API step. Validate attendees and timezones in a separate step to avoid mis-scheduling.

Adapting prompts for Claude vs ChatGPT (practical differences)

Both Claude and ChatGPT are excellent, but there are practical differences in 2026 that affect prompt choices:

• Claude (Anthropic) strengths: Longer context windows (useful for multi-step discovery), better at adhering to instruction constraints, and tools like Cowork make desktop file access and automation easier. Use Claude for heavier context-state micro-apps and strict JSON enforcement.
• ChatGPT strengths: Broad ecosystem integrations (Plugins, Microsoft Copilot ecosystem) and fast fine-tuning via system messages. Use ChatGPT for conversational UX-first micro-apps and when you need plugins for booking or CRM writes.

Common adaptation tips:

• For Claude, prefer explicit role and rule-based system prompts. Ask Claude to "strictly output JSON only" when feeding into parsers.
• For ChatGPT, use "function calling" (if available) or set up a low-code parsing step to handle natural-language variance.

Examples: Plugging prompts into no-code platforms (step-by-step)

Use case: Non-dev builds a restaurant recommender micro-app

1. Create an Airtable base with restaurants and fields (price, tags, url, geo).
2. Add a Webhook trigger from a chat widget (Typeform, Crisp, Intercom).
3. Run the Discovery prompt in ChatGPT/Claude to capture preferences — store results back in Airtable.
4. Call the Recommendation prompt with embedded top-20 filtered dataset (Airtable view). Receive JSON ranked list.
5. Show top 3 in the chat with buttons that call booking links or add to calendar via Zapier/Make.
6. Log click-throughs and conversion in your analytics tool to measure ROI; case studies like Bitbox show how instrumentation improves retention and cost management.

Implementation snippet: Simple fetch to ChatGPT function (example)

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:[{role:'system',content: '...'}, {role:'user',content:'...'}],
    temperature:0.1
  })
})

Note: For Anthropic Claude, use the Claude-specific API and include the system role per their docs. In a no-code flow, this fetch is handled by a "HTTP request" module and responses are parsed into fields.

Validation, monitoring and cost control

Micro-apps must be reliable and cost-effective to scale. Follow these practices:

• Add a validation layer: No-code platforms should run simple checks (required keys, ISO timestamps) before committing to external APIs. See the Marketplace Safety & Fraud Playbook for ideas on validation and defensive checks.
• Instrument intent metrics: Track conversion rate (recommendation → click), schedule success, and time-to-decision.
• Control token cost: Keep context minimal. For large catalogs, use vector search for top-K candidates then call the model to rank or justify only those candidates.
• Fail gracefully: Provide a fallback conversational path if the model returns invalid JSON or cannot find matches.

Advanced strategies and 2026 trends

Use these advanced techniques to push micro-apps beyond prototypes into production-grade solutions.

1. Agents + local context (desktop integrations)

Anthropic’s Cowork and Claude Code capabilities in late 2025–early 2026 made it practical to give AI safe, limited desktop and file access. For knowledge-heavy micro-apps (document discovery, internal recommendations), run agent patterns where the model reads a sandboxed file store, extracts metadata and returns JSON. Always scope permissions and log all file accesses for compliance.

2. Hybrid ranking: vectors + prompt ranker

Use vector search to narrow a catalog to ~20 items, then use a prompt-based ranker (low-temperature) to produce explainable, audited recommendations. This saves cost and improves determinism.

3. Prompt versioning and reuse

Store prompt templates as versioned assets (Airtable/Notion). Track which prompt version was used for a specific recommendation and A/B test different phrasing. In 2026 observability for prompts is considered best practice; see modular publishing workflows for versioning patterns.

4. Security, privacy and compliance

Because micro-apps often touch PII and calendars, apply these guardrails:

• Use enterprise LLM instances or on-prem options for sensitive data.
• Redact or pseudonymize PII before sending to third-party models.
• Keep an audit log of all model prompts and outputs for 90+ days.

Checklist: From prompt to production in 7 steps (non-developer friendly)

1. Pick template (Discovery / Recommendation / Workflow).
2. Map your data fields to template keys.
3. Wire a trigger (chat widget, form, webhook).
4. Send prompt to model with JSON output instruction.
5. Validate response and map to UI actions (buttons, calendar events).
6. Instrument analytics (clicks, conversions, cancellations).
7. Iterate using prompt versioning and A/B test phrasing.

Real-world examples & case studies (brief)

Where2Eat (anecdote from 2024–2025) is a useful mental model: a founder without formal engineering resources used a conversational discovery flow + a small recommendation engine to resolve group decision fatigue. In 2026, enterprise teams are taking the same pattern to internal use cases: sales-rep recommendation micro-apps that suggest next-best-actions, and HR scheduling micro-apps that propose interview slots across internal calendars using Claude agents or ChatGPT plugins.

Quick reference: Best prompts and patterns

• Best recommendation prompt: Low temperature, explicit scoring, JSON-only output, embed small dataset or use vector top-K.
• Best workflow prompt: Strict ISO timestamps, convert timezone in-prompt, return action payloads (calendar.create / webhook URL).
• Best discovery prompt: Minimal questions, return null for unknowns, iterative follow-ups for missing fields.

Final thoughts — why this matters for teams in 2026

Micro-apps reduce delivery time and engineering burden while increasing focus on measurable outcomes. By using a curated prompt library and no-code connectors, non-developers can ship production micro-apps for recommendation, scheduling and workflows with enterprise-grade guardrails. This approach addresses the core pain points: speed to deploy, integration simplicity, and measurable ROI.

Start building: immediate next steps

Pick one micro-app use case from your backlog. Use the templates above to prototype a 1-click flow in one day: discovery → recommendation → action. Keep the first version simple, instrument conversions, then iterate.

Call to action: Ready to move from prototype to production? Download our ready-to-import Airtable templates, JSON prompt library and no-code wiring guides at bot365.co.uk/prompt-library — or book a 30-minute workshop and we’ll help convert one of your workflows into a production micro-app.

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