Prompt Library: Micro-App Starter Pack for Non-Developers (Dining App, Task Manager, Expense Tracker)

Stop rebuilding prompts from scratch — a micro-app starter pack non-developers actually use

Pain point: Teams and citizen developers waste days tuning prompts, wiring UIs and stitching data flows for tiny app ideas. The result: countless one-off scripts, poor governance, and apps that break in production.

In 2026 the fastest way to validate an idea or automate a routine is a micro app — a focused, single-purpose UI backed by a few reusable prompt patterns. This guide gives you a starter pack of prompt templates and conversation patterns non-developers used in 2025–2026 to build three micro apps quickly with Claude or ChatGPT: a Dining App, a Task Manager, and an Expense Tracker. Included: UI wiring prompts, data-handling prompts, example JSON schemas, and operational checks for governance and versioning.

The micro-app moment (2026): why this matters now

Late 2025 and early 2026 saw major advances in model tool-use, desktop agent frameworks, and first-class APIs for structured outputs. Anthropic's Cowork preview brought powerful file and desktop automation to non-technical users; OpenAI's matured function-calling and realtime APIs enabled deterministic data exchange; and both Claude and ChatGPT models improved at returning strict JSON for UI rendering. For practical patterns on running resilient ops and prompt governance, see Resilient Freelance Ops Stack.

That means non-developers can orchestrate UIs, persistent storage (Airtable, Google Sheets, internal APIs), and multi-step conversational logic with minimal code — provided they have reusable, well-documented prompt templates. The starter pack below operationalizes those patterns.

How to use this starter pack

1. Pick a micro app (Dining, Tasks, Expenses).
2. Choose your runtime — no-code builder (e.g., Bubble, Make, Zapier), lightweight web shell (Glitch, Replit), or a desktop agent (Cowork / Claude on desktop) — desktop agents and edge-assisted collaboration are covered in field playbooks like Edge-Assisted Live Collaboration.
3. Wire the UI with the UI-wiring prompt that returns JSON component specs.
4. Connect storage using the data-handling prompts (Airtable/Sheet/REST).
5. Iterate with the conversation patterns below — init, add, edit, confirm, error-handling.

Reusable conversation patterns (applies to all micro apps)

Below are canonical prompt patterns every micro app needs. Save these as template objects in your prompt library with metadata (purpose, version, author, inputs, outputs, tests).

1. Init / Greeting (stateful launch)

Purpose: Establish context, return minimal JSON for UI, and list next actions.

{
  "system": "You are a concise assistant that always returns a JSON payload describing UI components and the next allowed actions.",
  "user": "Start a new session for {{appName}} with userPrefs={{userPrefs}}"
}

Expected JSON keys: components (list), actions (list), state (object).

2. Form Flow (collect structured input)

Purpose: Ask only for missing fields, validate, and return a single JSON object for persistence.

{
  "system": "Return only JSON. Validate user input against schema and prompt for missing fields. If invalid, include errorHints.",
  "user": "Create a new task with inputs={{inputs}} and schema={{taskSchema}}"
}

3. Confirmation & Undo

Purpose: Confirm destructive actions; provide an undo token to revert the last change.

{
  "system": "Limit output to confirmation JSON. Provide a reversible action_id and a summary sentence for the UI.",
  "user": "Delete item {{itemId}}?"
}

4. Error handling & fallback

Purpose: Return a clear error JSON, suggested fix, and a safe fallback action (e.g., retry, ask clarification, or escalate to human).

Practical tip: Always design the model to return structured error objects. This lets your UI show specific messages without manual parsing.

Starter App 1 — Dining App (Where2Eat style)

Goal: Recommend restaurants for a group based on preferences, constraints, and shared history. Non-developers used this template to build group decision tools in 1–3 days.

Data model (example)

{
  "restaurant": {
    "id": "uuid",
    "name": "string",
    "cuisine": "string",
    "price_tier": "1-4",
    "distance_miles": "number",
    "tags": ["tag"],
    "popularity_score": "0-100"
  }
}

UI-wiring prompt (returns component spec)

System: You are a UI spec generator. Output only JSON describing the minimal UI to collect group size, cuisine preferences, budget and location.
User: Build UI for Dining App session with savedRestaurants={{savedRestaurants}} and lastChoices={{lastChoices}}.

Example JSON output keys: components (inputs: multi-select cuisines, slider price, toggle delivery), suggestedAction (e.g., "Recommend 5 options").

Recommendation prompt (business logic)

System: You are a recommendation engine. Score each restaurant by alignment with constraints, recency (lastVisited boost), and popularity. Return top N with reasons.
User: Recommend top 5 restaurants for users={{groupPrefs}} constraints={{time, distance_miles, price_tier}} restaurants={{restaurantList}}

Example integration (Airtable/Sheets)

Wire a Zap/Make scenario: UI collects inputs -> Model returns JSON of top choices -> Zap writes vote tokens to Airtable -> Group votes update popularity_score -> Model re-runs recommendation. Use the model's deterministic JSON to avoid parsing errors.

Starter App 2 — Task Manager (personal micro PM)

Goal: Quick capture, triage and follow-up for tasks using natural language plus structured tags.

Data model (example)

{
  "task": {
    "id": "uuid",
    "title": "string",
    "description": "string",
    "priority": "low|medium|high",
    "due_date": "ISO8601|null",
    "status": "todo|doing|done",
    "tags": ["string"]
  }
}

Natural-to-structured prompt (capturing)

System: Convert the user's natural language into the task schema. Output only JSON. If a field is missing, set it to null and add a missingFields array.
User: Capture task from: "Follow up with procurement about server lease next Wednesday, mark high priority"

Conversation pattern: Quick Add vs Detailed Add

• Quick Add: model extracts title + inferred due date + priority.
• Detailed Add: the model asks for missing fields via a form flow and returns complete JSON.

Syncing and conflict resolution

When multiple devices update tasks, your prompt should produce a deterministic merge plan. Example:

System: Given localChanges and remoteState, return one of ["acceptLocal","acceptRemote","merge"] and a mergedObject if merging.
User: Resolve conflict for taskId={{id}} localChanges={{local}} remoteState={{remote}}

Starter App 3 — Expense Tracker

Goal: Capture expenses using receipts or free text, categorize them, and produce weekly summaries and budget alerts.

Data model (example)

{
  "expense": {
    "id": "uuid",
    "amount": "number",
    "currency": "string",
    "merchant": "string",
    "date": "ISO8601",
    "category": "string",
    "notes": "string"
  }
}

Receipt / free-text parsing prompt

System: Parse input to expense schema. Support receipts (line items) and single-line input. Normalize currency to USD if ambiguous, and include confidence per field.
User: Parse: "Lunch at Panera, $12.75, 2026-01-10"

For OCR and multi-channel receipt parsing workflows, see Omnichannel Transcription Workflows.

Reporting prompt (budget alerts)

System: Given monthlyBudget and recentExpenses, return an alert JSON if projected spend exceeds threshold. Include suggestions to reduce spending.
User: Check budget for category Travel with monthlyBudget=500 and expenses={{list}}

Concrete Claude vs ChatGPT prompt examples

Both Claude and ChatGPT can return structured JSON — but their exact system/user phrasing and tool integrations differ. Save provider-specific wrappers in your prompt library.

Claude example (strict JSON output)

System: You are Claude. Only output valid JSON. Do not include commentary.
User: Create a UI spec for adding a restaurant with fields: name, cuisine, price_tier, tags.

ChatGPT example (function-calling style)

System: You are ChatGPT. Use the function addEntity(name, schema, data) when creating items.
User: addEntity("restaurant", schema, data)
// Expect function call with JSON payload that our app will handle.

Prompt library hygiene: store, version, test, audit

To scale micro apps across teams you need governance. Adopt a lightweight PromptOps practice and these patterns:

• Store prompts as code: JSON/YAML files with metadata (id, version, author, createdAt, tests, providerVariants) — treat them like templates-as-code as explained in modular publishing workflows.
• Versioning: Use Git or a prompt registry; adopt semantic versioning for compatibility (e.g., v1.2.0).
• Unit tests: Keep a test harness that runs sample inputs against models and asserts JSON schema validity and a deterministic output signature — tie this into observability for workflow microservices (observability).
• Audit logs: Record prompt IDs and versions with each model call for compliance and retracing decisions (use RAG and logging patterns seen in perceptual AI projects like perceptual AI + RAG).
• Human-in-the-loop: For destructive or high-risk flows (e.g., refunds), require a sign-off prompt variant that includes an approval token — follow augmented oversight for approval gates.

Operational checklist before you release a micro app

1. Schema validation: add JSON Schema for all model outputs and run automated checks.
2. Rate limits & retries: wrap model calls in exponential backoff and cache static results (e.g., restaurant lists) — also consider cloud cost implications and optimization patterns from cloud cost optimization.
3. Data privacy: redact PII in prompts and use ephemeral context where required.
4. Monitoring: log model responses, latencies, and parse failures to an observability dashboard (see observability playbooks).
5. Rollback plan: keep old prompt versions accessible and tagged so you can revert quickly — include rollback steps in your ops stack like those in resilient ops.

Real-world case: Rebecca Yu's dining app (what worked)

Rebecca Yu's Where2Eat is a useful micro-case that mirrors many lessons here: start small, iterate with users, and use the model for both recommendation and orchestration. Key wins were:

• Simple structured inputs (avoid over-asking).
• Persisted group preferences for better recommendations.
• Versioned prompt templates so she could try different recommendation heuristics without breaking the UI.

Advanced strategies and future predictions (2026+)

Expect the following trends:

• Model-led UI generation: Desktop agents and copilots will increasingly render UI from model-returned JSON; your prompt library becomes the app's schema source of truth — tools like Compose.page point toward this future.
• Interoperable prompt registries: Standard formats (YAML + JSON Schema + tests) will let organizations share and audit prompt modules like code libraries.
• Hybrid governance: Combine lightweight automated tests with human approval gates for critical prompts — see augmented oversight references (augmented oversight).
• On-device integrations: Expect more on-device voice and privacy-first features; review tradeoffs in on-device voice for web interfaces.

Sample prompt library entry (YAML conceptual)

id: dining-ui-v1
version: 1.0.0
author: jane.doe@company.com
providerVariants:
  claude:
    system: "Only output JSON UI specs."
    userTemplate: "Build UI for Dining App with {{savedRestaurants}}"
  chatgpt:
    system: "Prefer function calls add_ui_spec(...)"
tests:
  - name: returns_components
    input: { savedRestaurants: [] }
    assert: output.components.length > 0

Actionable takeaways

• Save these conversation patterns into a prompt library with metadata and tests before you build your first micro app.
• Always have the model output structured JSON for your UI — this reduces parsing errors by 90% in practice.
• Use provider-specific wrappers: keep a Claude prompt and a ChatGPT prompt variant for the same logical template.
• Automate prompt testing: run a daily harness to catch regressions when models change.

Quick reference: three copy-paste prompt templates

Dining recommendation (Claude)

System: You are Claude. Output JSON only. Score restaurants by match to constraints and return top 5. Include a reason for each pick.
User: Recommend restaurants for constraints={{constraints}} restaurants={{restaurantList}}

Task capture (ChatGPT function-calling)

System: Use function create_task(payload) to send structured task objects.
User: "Call create_task with the parsed fields from: 'Email legal about contract extension tomorrow, high priority'"
// Expect a function call with the task JSON

Expense parsing

System: Parse the following text into an expense JSON with confidence scores per field. Output only JSON.
User: "Uber $23.40 2026-01-05"

Closing: ship small, govern well, iterate fast

Micro apps are the fastest path from idea to impact in 2026 — but only if you stop treating prompts as throwaway text. Make them first-class artifacts: store them, version them, test them, and provide clear provider variants. Use the starter pack and conversation patterns above to get a Dining App, Task Manager, or Expense Tracker live in hours, not weeks. And when you graduate from a micro app to a product, your prompt library will be your most valuable asset.

Next steps: Clone the starter templates into your prompt registry, run the provided test harness, and try a live integration with Airtable or Google Sheets today.

Call to action

Ready to accelerate your team’s micro-app output? Download the starter prompt pack and JSON schemas, or schedule a walkthrough with an prompts engineer to adapt these templates to your stack. Ask for the 2026 Prompt Library Kit — includes provider wrappers, YAML schema examples, and CI test scripts.

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