Ship a micro-app in a week: a starter kit using Claude/ChatGPT

Beat decision fatigue and pipeline chaos: ship a micro-app in a week

You know the pain: a bright idea backed by an LLM, but months of yak-shaves—CI flakiness, half-assembled infra, and a dozen tools—stand between idea and users. This guide gives you a practical, low-risk 7-day plan, a starter repo layout, and ready-to-run CI/CD recipes to take a small LLM-driven micro-app (think: a dining recommender) from idea to running in a containerized, GitOps-friendly environment in one week.

Why this matters in 2026

Micro-apps are now mainstream. People with little formal engineering experience are using Claude, ChatGPT, and other LLMs plus simple container pipelines to build highly personal tools in days, not months. Anthropic's 2025 previews (Cowork / Claude Code) and OpenAI's continued improvements to developer APIs pushed LLM-first app creation into production patterns. The result: the tooling expectations are different — teams expect fast iteration, secure LLM usage, and reproducible CI/CD that doesn’t explode as usage grows.

In short: we need repeatable starter kits that combine LLM integration, container builds, and automated deployment with secure secrets handling and minimal ops overhead.

What you'll build in seven days

A minimal micro-app: a containerized web service that calls an LLM (Claude or ChatGPT) to recommend restaurants based on a small user profile. Features:

• HTTP API (OpenAPI-compatible) and tiny web UI
• LLM adapter layer supporting Claude and ChatGPT with a shared interface
• Dockerfile and GitHub Actions for CI + Docker image publish
• Kubernetes manifests and a GitOps manifest for ArgoCD/Flux
• Secrets stored in GitHub Actions secrets or SealedSecrets/Vault
• Local dev via Docker Compose

Starter repo layout (suggested)

llm-microapp-starter/
  ├─ app/                      # FastAPI (or Express) service
  │  ├─ main.py
  │  ├─ llm_adapter.py         # abstracts Claude/ChatGPT API calls
  │  ├─ requirements.txt
  │  │  └─ Dockerfile
  ├─ web/                      # optional tiny frontend
  │  └─ index.html
  ├─ infra/
  │  ├─ k8s/                   # Kubernetes manifests
  │  └─ gitops/                # ArgoCD/Flux app manifests
  ├─ .github/workflows/ci.yml  # build & push images, run tests
  ├─ docker-compose.yml
  └─ README.md
  

7-day practical plan — day-by-day

Follow these daily milestones. Each day focuses on concrete outputs you can test and iterate on.

Day 1 — Define scope & scaffold

• Define a strict MVP: user submits preferences, service returns 3 restaurant recommendations with a short reason.
• Pick tech: Python + FastAPI for speed, or Node + Express for JS shops. We'll use FastAPI examples below.
• Scaffold the repo using the layout above. Add README, license, and a small design doc (one page) describing inputs, outputs, and API contract.

Day 2 — LLM adapter and mock

Build an adapter with a single interface so you can switch between Claude and ChatGPT without changing business logic.

# app/llm_adapter.py (Python sketch)
  from typing import Dict
  
  class LLMAdapter:
      def __init__(self, provider: str, api_key: str):
          self.provider = provider
          self.api_key = api_key

      async def recommend(self, profile: Dict) -> Dict:
          """Return structured recommendation data."""
          if self.provider == 'chatgpt':
              return await self._call_chatgpt(profile)
          elif self.provider == 'claude':
              return await self._call_claude(profile)
          else:
              raise RuntimeError('Unsupported provider')
  

Create mocks for unit tests so you can iterate the API without hitting an LLM and incurring cost. Unit testing the adapter can be combined with a schema-first approach to keep your contract stable as you iterate.

Day 3 — Implement API & local UI

• Implement FastAPI endpoints (/recommend) and a tiny single-page app to call the API.
• Make responses deterministic in dev by using a mock LLM with canned outputs.
• Add basic input validation and OpenAPI docs (FastAPI does this automatically).

Day 4 — Containerize and local dev

Write a small, secure Dockerfile and docker-compose for local dev. Keep the image size low and avoid including secrets.

# app/Dockerfile
  FROM python:3.11-slim
  WORKDIR /app
  COPY requirements.txt .
  RUN pip install --no-cache-dir -r requirements.txt
  COPY . .
  CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8080"]
  

Run locally: docker-compose up --build and test the web UI. Verify you can switch between mock and real LLM backends via env var.

Day 5 — CI: tests, build, and publish

Set up CI that runs unit tests, builds a Docker image, and publishes to a registry only on main branch. Keep the workflow simple — faster iterations beat a fragile 20-step pipeline.

# .github/workflows/ci.yml (essential parts)
  name: CI
  on: [push]
  jobs:
    test-build:
      runs-on: ubuntu-latest
      steps:
        - uses: actions/checkout@v4
        - name: Set up Python
          uses: actions/setup-python@v4
          with: python-version: '3.11'
        - name: Install deps
          run: pip install -r app/requirements.txt
        - name: Run tests
          run: pytest -q
        - name: Build and push image
          if: github.ref == 'refs/heads/main'
          uses: docker/build-push-action@v4
          with:
            push: true
            tags: ${{ secrets.REGISTRY }}/llm-microapp:${{ github.sha }}
  

Use ephemeral tags for PR builds, and only push a stable tag on merge. For CI and repo-driven deployments consider treating your GitOps repo as the single source of truth and updating it from Actions instead of pushing images from ad-hoc scripts.

Day 6 — Kubernetes manifests & GitOps

Keep manifests minimal. Use a Deployment, Service, and optional HorizontalPodAutoscaler. Add a GitOps app manifest for ArgoCD/Flux that points to the infra manifest directory.

# infra/k8s/deployment.yaml (essential parts)
  apiVersion: apps/v1
  kind: Deployment
  metadata:
    name: llm-microapp
  spec:
    replicas: 1
    selector:
      matchLabels:
        app: llm-microapp
    template:
      metadata:
        labels:
          app: llm-microapp
      spec:
        containers:
          - name: app
            image: ghcr.io/your-org/llm-microapp:latest
            ports:
              - containerPort: 8080
            envFrom:
              - secretRef:
                  name: llm-secrets
  

On-cluster secrets: use sealed-secrets, SOPS, or your cloud provider secret store. Avoid putting API keys in Git. If you need to balance latency and cost across regions, consider hybrid edge–regional hosting strategies to place inference closer to users.

Day 7 — Smoke tests, monitoring, and secure rollout

• Run a canary or blue-green rollout. Keep the initial replica count small and limit request rates to your LLM provider to control costs.
• Add a simple healthcheck and uptime probe, and a single Prometheus metric for request latency and LLM cost per request. For selecting monitoring tooling, see recent monitoring platform reviews.
• Run a smoke test: call /recommend with a test profile and assert the response schema and latency threshold.

CI/CD recipes — concrete examples

GitHub Actions: build, test, and deploy (container-only)

# Key steps demonstrated earlier; add CD step to update image tag in GitOps repo
  - name: Update GitOps manifest
    if: github.ref == 'refs/heads/main'
    run: |
      git clone https://github.com/your-org/gitops-repo tmp-gitops
      cd tmp-gitops
      yq eval '.spec.source.targetRevision = "${{ github.sha }}"' -i apps/llm-microapp.yaml
      git add . && git commit -m "image ${GITHUB_SHA}" || true
      git push
  

This pattern keeps your infra repo as the source of truth. ArgoCD/Flux watches the repo and applies changes. If you need to maintain schema compatibility across API changes, a live schema and migration strategy can help avoid breaking clients.

Alternative: Direct deploy to Kubernetes (small teams)

If you don't run GitOps, a small teams can use kubectl in Actions to apply manifests. This is less auditable but quicker to iterate:

- name: Set kubeconfig
  - name: kubectl apply
    run: kubectl set image deployment/llm-microapp app=${{ secrets.REGISTRY }}/llm-microapp:${{ github.sha }} --namespace default
  

Secure LLM usage — best practices

• Secrets management: never store model API keys in your repo. Use GitHub Actions secrets, Kubernetes secrets encrypted with SealedSecrets, or cloud secret managers (AWS Secrets Manager, GCP Secret Manager).
• Rate-limits & cost controls: implement client-side rate limiting and budget guards. Track tokens/requests per session and surface warnings in logs and alerts. For building resilient flows that account for rate-flooring and provider costs, see resilient transaction patterns.
• Input sanitization: validate user inputs to avoid LLM prompt injection and PII leakage.
• Response filtering: use a safety layer to detect hallucinations, profanity, or policy violations before exposing content to users.

Testing and observability

• Unit test the adapter with mocked responses for both Claude and ChatGPT.
• Integration test using a low-cost sandbox LLM key or replay recorded responses.
• Monitoring: export request_count, request_latency_seconds, llm_tokens_used_total, and llm_cost_estimate to Prometheus. See curated reviews for selecting the right tools: monitoring platform reviews.
• Use structured logs (JSON) so errors and LLM call metadata are searchable in your log system.

Cost control patterns

• Cache LLM responses for repeated queries (LRU for 24h) to avoid repeated token consumption on the same input — caching also improves edge performance and perceived latency.
• Use a short, structured output schema from the model so you can request concise responses and reduce tokens.
• Batch requests where possible (for background recomputation), and use cheaper embedding or retrieval models when only semantic search is required.

Advanced strategies & 2026 trends

As of late 2025 — early 2026 we see three trends you should build for today:

1. Hybrid multimodal agents: LLMs are increasingly used with structured tools (APIs, SQL, search) via agent patterns. Design the app so the LLM is a layer, not the single source of truth.
2. On-device inference & federated patterns: smaller specialist models running at the edge (for privacy-sensitive data) are becoming viable. Keep your app modular to swap in an edge model later.
3. Stronger GitOps & policy automation: teams use ArgoCD/Flux + OPA/Gatekeeper policies to automate compliance. If your micro-app must meet org policies, add policy-as-code early.

Example: Minimal FastAPI app (summary)

# app/main.py (high-level)
  from fastapi import FastAPI
  from llm_adapter import LLMAdapter

  app = FastAPI()
  adapter = LLMAdapter(provider='chatgpt', api_key='ENV_VAR')

  @app.post('/recommend')
  async def recommend(profile: dict):
      # validate profile
      result = await adapter.recommend(profile)
      return result
  

Operational checklist before you call it "done"

• Unit & integration tests passing in CI
• Image builds reproducibly and runs in local k8s (kind/minikube)
• Secrets stored securely and rotated on deploy
• Cost guardrails installed and telemetry enabled
• GitOps flows tested: change in infra repo triggers rollout
• Post-deploy smoke test and alerting configured

Real-world example & inspiration

Rebecca Yu built a dining app in a week using Claude and ChatGPT assistance — a growing example of "vibe coding" where non-traditional builders create micro-apps for personal use (TechCrunch / Substack examples from 2024–2025). The key lesson: start small, iterate fast, and automate the boring parts.

Common gotchas and how to avoid them

• Too many tools: keep the stack minimal for the first pass—API server, adapter, container, and GitOps. Add observability and policy tools only when needed.
• Leaky secrets: never commit keys. Use pull request previews with ephemeral keys or mocks.
• Unbounded LLM costs: implement per-user and global quotas and track token consumption in telemetry.
• Fragile CI: keep workflows short and idempotent. Prefer build-on-merge and transient artifacts for PR checks.

Where to extend after week one

• Personalization: add embeddings + vector DB for user memories and stateful recommendations.
• Multi-model routing: route prompts to Claude for safety-sensitive responses and ChatGPT for creative outputs.
• Analytics: A/B test prompt templates and track downstream user conversions.
• Edge/offline mode: run a smaller local model for basic recommendations when privacy or offline mode is required.

Actionable takeaways

• Ship a working LLM micro-app within 7 days by focusing on the LLM adapter, a containerized API, and a simple GitOps or kubectl deploy path.
• Start with mocked LLM responses in dev to avoid cost and speed up iteration.
• Automate image builds and use a GitOps repo to keep infra changes auditable.
• Protect secrets, implement rate-limits, and add telemetry for both performance and cost visibility.

Call to action

Ready to ship? Clone the starter kit repo (github.com/your-org/llm-microapp-starter), follow the 7-day plan in this article, and open a PR with your favorite prompt template. If you want a pre-configured GitHub Actions + ArgoCD setup tailored to your cloud, contact our team at deployed.cloud for a vetted, production-hardened starter that removes the common blockers and gets you to live in a week.

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