micro‑appstemplatesproductivity

A Template Micro‑App: Recreating the 'Dining App' as an Internal Productivity Tool

aassign

2026-02-03

9 min read

A reusable template to build tiny decision‑help micro‑apps: data model, rule engine, and LLM prompts to ship in days.

Beat decision fatigue and slow workflows with a tiny, powerful micro‑app

If your teams waste hours debating where to meet, which vendor to pick, or who should own the next on‑call shift, you don’t need another monolith — you need a micro‑app template that teams can deploy in days, not months. In 2026, with advanced LLMs, policy engines, and low‑code UIs maturing, recreating a tiny 'Dining App' as an internal decision‑help micro‑app is one of the fastest ways to reduce friction and standardize choices across engineering and ops.

What this guide gives you (quick)

Reusable data model for decisions, users, preferences, and audit trails
Rule engine patterns and sample rules for deterministic routing
LLM prompt templates for contextual suggestions plus structured JSON outputs
Integration recipes for Slack, Jira, GitHub, and event sources
Security & compliance checklist for auditable decisioning
Step‑by‑step rapid build plan so non‑devs can own micro‑apps

The 2026 context: why a tiny 'Dining App' pattern matters now

Late 2025 and early 2026 solidified several trends: LLMOps became mainstream, low‑latency model APIs moved to enterprise clouds, and policy engines (OPA variants) were deployed in more organizations for governance. At the same time, teams adopted 'micro' apps — purpose‑built, ephemeral tools — to solve single decision problems without expanding codebases. The Dining App is an archetype: it maps users, constraints, and preferences to ranked options. That archetype translates directly to internal workflows like vendor selection, meeting location, on‑call pairing, and quick triage routing.

Design principles: keep it tiny, explainable, and auditable

Tiny: Solve one decision. Keep UI and logic minimal.
Explainable: Combine deterministic rules with LLM suggestions and show why an option was chosen.
Auditable: Log inputs, rules, model version, and output so decisions are traceable for SLAs and compliance. See guidance on reconciling vendor SLAs when your decision impacts third‑party uptime.
Composable: Integrate with existing tools (Slack, Jira, GitHub) via webhooks and function calls — and keep the integration surface minimal so you can deploy quickly using the same starter patterns in the one‑week micro‑app kit.

Core data model (reusable)

Below is a compact JSON schema you can adapt. The schema balances expressiveness and simplicity so non‑devs can edit preferences and admins can add rules.

{
  'DecisionItem': {
    'id': 'string',
    'type': 'string',          // e.g., 'restaurant', 'oncall', 'room'
    'name': 'string',
    'attributes': { /* arbitrary key-values for filtering */ }
  },
  'User': {
    'id': 'string',
    'name': 'string',
    'team': 'string',
    'location': 'string',
    'skills': ['string'],
    'workloadScore': 0.0
  },
  'Session': {
    'id': 'string',
    'requesterId': 'string',
    'context': { /* freeform context */ },
    'time': 'ISO8601'
  },
  'AuditRecord': {
    'id': 'string',
    'sessionId': 'string',
    'input': {},
    'rulesSnapshot': 'url-or-hash',
    'modelVersion': 'string',
    'result': {},
    'timestamp': 'ISO8601'
  }
}

Key notes:

DecisionItem.attributes lets you add domain filters like cuisine, cost, SLA impact, or on‑call skill tags.
workloadScore is a floating score used by rule engine to avoid over‑assignment — the same matching concept used in micro‑matchmaking and short‑form hiring projects.
rulesSnapshot stores a pointer to exact rule definitions (versioned). Treat it like a repo pointer and back it up with safe backups and versioning best practices (automating safe backups).

Rule engine architecture: deterministic core + LLM augmentation

In 2026 the best practice is a dual‑layer approach:

Deterministic rule engine evaluates hard constraints — availability, policy, legal restrictions, SLA thresholds. Use a lightweight rule engine or policy framework (Open Policy Agent or a hosted rules service).
LLM augmentation provides ranked suggestions when multiple valid options remain; it explains tradeoffs in human terms and surfaces soft preferences.

Sample rule set (JSON/YAML)

- id: rule_1
  name: avoid_overassign
  description: do not pick users with workloadScore > 0.8
  condition:
    field: User.workloadScore
    op: lt
    value: 0.8
  action: allow

- id: rule_2
  name: must_have_skill
  description: chosen candidate must match required skill tag
  condition:
    field: DecisionItem.attributes.requiredSkill
    op: in
    value: User.skills
  action: allow

- id: rule_3
  name: proximity_preference
  description: prefer users in same location
  condition:
    field: User.location
    op: equals
    value: Session.context.location
  action: weight=+0.2

Execution pattern:

Filter candidates via allow/deny rules.
Apply deterministic weights (e.g., workload penalty).
Pass top N candidates into LLM for final ranking and human‑readable rationale.

Rule engine implementation patterns

Use OPA/Rego for policy‑heavy orgs; store policies in Git and CI them. If you need edge‑level policy execution, consider edge registries and cloud filing patterns to keep rules close to where decisions happen.
For rapid dev, keep rules as JSON/YAML evaluated by a tiny rule interpreter (Node/Python) — this is the same pragmatic pattern in the one‑week micro‑app starter.
Use WebAssembly (WASM) engines at the edge for low latency if the micro‑app is chatty; WASM is an easy fit with edge registries and lightweight engines (edge registries).

LLM prompts: structured, safe, and reversible

LLM prompts in 2026 should be treated like code. Version them, test them, and keep them small. Combine deterministic facts with a constrained generation format (JSON or function calls). Below are practical prompt templates.

System prompt (example)

You are a decision assistant for internal workflows. Inputs are JSON with: session, requester, candidates. Return a JSON object: {'ranking': [{'id': 'candidateId', 'score': float, 'reason': 'text'}], 'explanation': 'text'}. Use concise technical rationales. Be conservative about hallucinations; if uncertain, state limitations.

User prompt (example) — dining app style

Input:
{
  'session': {'id':'s1','location':'SF','time':'2026-01-17T12:00:00Z'},
  'requester': {'id':'u1','preferences':['vegan','quick']},
  'candidates': [{'id':'r1','name':'SpotA','attributes':{'cuisine':'vegan','walkTime':10}},{...}]
}

Task: Rank candidates for the requester. Provide score 0-1 and a short reason for each. Also include one short suggestion for fallback (e.g., open hours, booking required).

Internal assignment prompt (example)

Input: JSON (session, requiredSkill, candidates[], SLA_impact)

Task: From candidates, return a top 3 ranked list with scores and reasons that cite: skill match, workload adjustment, timezone/proximity, and policy constraints. Output strictly as JSON with fields: ranking[], explanation (single string).

Use model settings:

temperature: 0–0.2 for deterministic results
max_tokens: keep concise (200–400) and rely on structured output
enable function calling if your model supports it to enforce JSON output — function calls are especially useful when you wire the starter repo or the one‑week kit.

Integration patterns (rapid, secure)

Make your micro‑app composable with existing tools. Typical event flows:

User triggers (Slack slash command /retable) or a Jira transition.
Webhook invokes a microservice (serverless function) that loads data model and rules.
Serverless runs rule engine; if multiple candidates remain, call LLM for ranking.
Respond to Slack, and optionally create a Jira ticket or GitHub issue documenting the decision and linking the AuditRecord.

Example: Slack -> rule -> LLM -> Jira

Slack slash command: /where2eat
Lambda: load session, apply rules, call model
Return: ephemeral Slack message with top 3 and a button to create a Jira task 'Book table at X'
On button click: create Jira issue, attach AuditRecord URL, store modelVersion and rulesSnapshot (backed by safe repo versioning — see backup & versioning).

Security, governance & audit (non‑negotiables)

By 2026, regulators and auditors expect model provenance and policy enforcement. For internal micro‑apps, follow this minimal set:

Log the input JSON, rule snapshot (hash or commit), model version, and the exact LLM response.
Encrypt logs at rest and restrict access via RBAC; treat AuditRecord as a legal artifact. Store snapshots and logs with storage best practices (storage cost optimization).
Version prompts and store them in Git so you can roll back after a bad prompt change.
Use allow/deny rules for sensitive decisions (e.g., HR assignments) and keep those rules deterministic — don't let an LLM overrule them.
Mask PII in prompts or use synthetic tokens for sensitive data before sending to third‑party models.
Run periodic audits for bias: check if workloads or locations disproportionately affect certain groups.

Pro tip: Store rules in the same repo as prompts and CI them together. A failing policy test should block deploys.

Testing, metrics, and continuous improvement

Treat micro‑apps like product features. Track these KPIs:

Decision latency (ms)
Acceptance rate (when users accept suggested option)
Reopen rate (times users override a suggestion)
Assignment fairness metrics (workload variance)

Testing checklist:

Unit tests for rule interpreter (edge cases like empty candidate sets)
Golden prompt tests — expected JSON structure and key rationale phrases
Integration tests for Slack/Jira flows using sandbox tokens
Smoke tests for audit logging and rule snapshotting

Step‑by‑step build plan (finish in a weekend)

Define the decision: document inputs, users, and candidate attributes.
Create the minimal data model (use the JSON above) in your datastore (Postgres/Dynamo).
Write the first set of deterministic rules (ban/allow + basic weights).
Implement a tiny rule interpreter (or deploy OPA). Add unit tests.
Write two LLM prompts: (1) ranking + reasons, (2) fallback/explanation. Version them in Git.
Wire a simple UI: Slack command or Retool page with a button to run decisioning.
Add audit logging: store request, rulesSnapshot hash, modelVersion, and result.
Run a pilot with one team for 1–2 weeks; capture metrics and feedback, iterate rules. If you want to accelerate the pilot, the starter kit and the patterns in the CRM-to-micro-app playbook are good accelerators.

Concrete examples: internal micro‑apps you can spin up from this template

Meeting Room Finder: Filter rooms by capacity, equipment, and booking policy; LLM suggests alternatives and short rationales.
On‑call Pairing: Use workloadScore, skills, and recent pager history to create pairs; LLM explains why pairings minimize burnout. See micro‑matchmaking patterns (micro‑matchmaking).
Interview Scheduler: Match interviewer skills to role needs while balancing calendars.
Vendor Shortlist: Produce a ranked short list with policy checks and a human‑readable brief for procurement.

Future‑proofing & scaling (2026+)

As your micro‑app fleet grows, shift from static weights to data‑driven scoring:

Collect outcome signals (did team accept? SLA met?) and feed them into a small ranking model.
Automate rule coverage analysis: run model suggestions, measure divergence from rules, and notify owners. Automation patterns and prompt chains can help here (prompt chains).
Adopt model governance systems (model registry, lineage, drift detection) — these are standard in enterprise LLMOps in 2026. Consider consortium approaches for verification and trust (interoperable verification).

Actionable takeaways (your checklist)

Start with a single decision and map inputs → candidates → outputs.
Implement a deterministic rule layer first; use LLMs to augment, not replace.
Version prompts and rules together; log everything to an auditable store.
Integrate with one chat tool (Slack) and one issue tracker (Jira) to get rapid adoption.
Measure acceptance and fairness; iterate rules monthly.

Quick reference: sample LLM prompt (copy & adapt)

System: You are a strict JSON generator. Given inputs, rank candidates 0..1 and explain briefly.

User: {'session':..., 'requester':..., 'candidates':[...]} 

Return: {'ranking':[{'id':'','score':0.0,'reason':''}], 'explanation':''}

Closing: why this matters for CTOs and team leads in 2026

Teams no longer accept long release cycles for tiny productivity gains. The micro‑app pattern — exemplified by the Dining App — gives product and ops teams a fast, governed, and auditable way to remove decision friction. By combining a simple data model, deterministic rules, and carefully versioned LLM prompts, you get the best of both worlds: fast developer velocity and enterprise grade control.

If you want to skip the wiring and get a battle‑tested template with Slack/Jira integrations, versioned prompts, and audit logging pre‑configured, we’ve packaged this exact pattern into a starter repo and deployment guide (starter template).

Ready to build your first micro‑app? Download the starter template, or schedule a short walkthrough with our team to adapt it to your internal workflow. Small app. Big throughput gains.

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