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Full Stack Engineer (AI) — Technical Assessment

Time limit: 2–3 hours max.

Challenge

Build a small internal dashboard to manage a catalog of indie games, plus an AI chat that answers natural-language questions about that catalog.

Requirements

  1. Games CRUD Fields: title, developer, genre, monthly_revenue_usd, monthly_downloads, average_rating. Persist locally (SQLite, JSON, or in-memory).

  2. REST API Expose the catalog via API routes.

  3. AI Chat Chat UI that answers questions like:

    • "Which game made the most revenue last month?"

    • "How many games are rated above 4?"

    • "Compare revenue of Game A vs Game B."

    • Must use tool calling / function calling to query the data. Do not dump the full DB into the prompt.

    • Responses must stream.

  4. UI Dashboard view (table or cards) + chat view. Tailwind is fine.

Stack

  • Next.js 14+ (App Router) + TypeScript
  • Tailwind CSS
  • LLM layer: your choice — Anthropic SDK, OpenAI SDK, Vercel AI SDK, OpenRouter, LangChain / LangGraph, etc. Any model/provider is fine as long as tool calling + streaming work.
  • Storage: your choice (SQLite, JSON, in-memory)

Out of scope

Auth, deployment, production error handling, full test coverage.

Implementation

Stack chosen

Layer Choice Reason
Framework Next.js 15 App Router + TypeScript Matches requirement; App Router enables Server Components
Styling Tailwind CSS + shadcn/ui (Sonner) Fast, consistent, dark-theme friendly
LLM OpenAI SDK (gpt-4o-mini) Tool calling + SSE streaming, cost-efficient
Storage SQLite via better-sqlite3 Zero-config, synchronous API, persists between restarts

Project structure

app/
  api/
    games/          → GET (list + filter), POST (create)
    games/[id]/     → GET, PUT, DELETE
    chat/           → POST — SSE streaming with tool calling
  chat/             → Chat page
  page.tsx          → Dashboard page
components/
  GameTable.tsx     → CRUD table (responsive: cards on mobile, table on desktop)
  ChatWindow.tsx    → Streaming chat UI with suggestion pills
  ui/toaster.tsx    → Sonner toast wrapper
lib/
  db.ts             → SQLite singleton, schema, seed, query helpers
  tools.ts          → 4 LLM tool definitions + handlers
  types.ts          → Shared TypeScript interfaces
  utils.ts          → cn() helper (tailwind-merge + clsx)

AI tools available to the LLM

Tool Description
get_games List all games, optionally filtered by genre or min rating
get_game_by_id Fetch a single game by ID
compare_games Compare two games by title (revenue, downloads, rating)
get_stats Aggregate stats: top revenue, top downloads, avg rating, genre breakdown

How to run locally

Prerequisites

  • Node.js 18+
  • An OpenAI API key

Setup

# 1. Install dependencies
npm install

# 2. Set your API key
echo "APENAI_API_KEY=sk-..." >> .env.local

# 3. Start the dev server
npm run dev

Open http://localhost:3000.

The SQLite database (games.db) is created automatically on first run and seeded with 12 indie games.

Trade-offs

  • better-sqlite3 over Prisma/Drizzle — synchronous API is simpler in Next.js Server Components; no async/await overhead. Trade-off: less ergonomic for complex queries.
  • Manual SSE over Vercel AI SDK — avoided adding a dependency for a feature (streaming + tool loop) that's 60 lines of code. Trade-off: more boilerplate.
  • gpt-4o-mini over GPT-4o — cost-efficient for a demo; handles multi-step tool use correctly. Trade-off: slightly weaker reasoning on ambiguous queries.
  • No pagination on API — pagination is client-side only (10 rows). Trade-off: all games load on first request. Acceptable at catalog scale (~100s of games).
  • Auth out of scope — per assessment requirements.

Mandatory: AI_WORKFLOW.md

See AI_WORKFLOW.md for the full documentation of how AI coding tools were used during this assessment.

Evaluation

Area Weight
Use of AI coding tools (AI_WORKFLOW.md) 30%
Full-stack implementation (CRUD + API + chat + tool use) 30%
Code quality 20%
UX 10%
README clarity 10%

How to submit

  1. Fork this repo.
  2. Create branch submission/<your-name>.
  3. Implement.
  4. Open a Pull Request to main of this repo.
  5. In the PR include: time spent, how to run locally, trade-offs made.