AI Software Intern – Internship Task
Document
Internship Role Overview
As an AI Intern for 6 months (full-time), you will engage in research-driven development
of Generative AI applications. The internship emphasizes both academic research and
hands-on implementation, contributing to real product development, exploring research
papers, and building internal tools.
● Company: Wasserstoff
● Role: AI Intern (Generative AI) – Full-Time, 6 Month Internship, Work From
Office(Gurugram)
● Focus: Research-based implementation of Generative AI tools and applications
(mix of research and real product development)
● Required Skills: Python; Transformers and LLM architecture; LangChain
framework; OpenAI API usage; system design fundamentals; basic database
knowledge (SQL/NoSQL)
Internship Task: Generative AI Backend & Interactive
Guessing Game
Objective
Build a minimum‑viable clone of the “What Beats Rock” concept that demonstrates
your ability to wire Generative AI into a production‑ready backend, apply caching &
data‑structures, and ship a one‑click Docker deployment. The frontend only needs to be
functional and lightly animated; the technical depth must live in the backend and infra.
Task Details
1. Core Game Logic
○ A round starts with a seed word (e.g., Rock). Users type something they
think "beats" it.
○ Forward the guess to a GenAI endpoint with a well‑crafted prompt asking
whether guess X beats word Y.
○ If the AI says YES ➜ add the guess to a linked‑list (or equivalent) that
stores every distinct answer in order, increment the user’s score, and
✅
respond with:
Nice! “Paper” beats “Rock”. Paper has been guessed
3 times before.
○ If the guess already exists in the linked list, immediately return
Game Over.
○ At any point the user can request a history of their own guesses (traverse
the list).
2. Global Guess Counter
○ Persist a per‑answer counter ("Paper → 42 total guesses so far") in your
database. Expose it in the response as above.
3. Caching Layer
○ Cache (input‑pair → AI verdict) so identical validations never hit the LLM
twice.
4. Concurrency & Rate Limits
○ Handle ≥100 simultaneous players without crashing. Use async I/O,
connection pooling, and sensible per‑IP rate limits.
5. Moderation & Persona
○ Filter profanity / disallowed content before sending it to the LLM.
○ Expose at least two host personas (e.g., Serious vs Cheery )
selectable via query param or header.
6. Frontend (lightweight)
○ Single‑page HTML or tiny React app.
○ Textbox for guesses, animated emoji feedback (confetti or on correct, on
Game Over).
○ Display current score, last five guesses, and the global guess‑count for the
most recent answer.
7. Deployment
○ Provide a Dockerfile and docker‑compose.yml that spin up API, DB, and
Redis with one click in Docker Desktop.
○ A bonus point if you additionally live‑deploy to a free tier (Render, Railway,
Fly.io) and include the URL.
Requirements
● Backend: Python + FastAPI or Node.js
● Database: PostgreSQL or MongoDB (use Docker image).
● Cache: Redis (for verdict & duplicate‑check caching).
● AI Provider: Any GenAI text endpoint reachable via REST; use env vars for keys.
● Data‑structure: Maintain a linked‑list (in memory or DB) for each game session
plus a global table/collection for aggregate counts.
● Testing: Minimum one end‑to‑end test covering duplicate‑guess game‑over path.
Deliverables
1. Source Code – backend + minimal frontend in a github repository. 2.
Docker Assets – Dockerfile, docker‑compose.yml, sample .env.
3. README – < 1 000 words: setup, how to play, architectural choices, prompt
design.
4. Tech Report (1 page) – caching strategy, linked‑list implementation, concurrency
handling metrics, the game logic,and a new feature you can implement.
Judging Criteria
Weight Category What We Look For
30 % Functionality Correct AI validation,
duplicate‑detection, global counters
25 % AI Integration Prompt quality, error resilience, cost/memory
awareness
15 % Performance Response latency, cache hit‑rate, load test results
10 % Deployment One‑click Docker success, bonus live URL
10 % Frontend UX Clear feedback, emoji/animation polish 10 % Code
& Docs Clean structure, comments, README
Recommended Folder Structure
genai-intern-game/
├── backend/
│ ├── api/ # FastAPI / Express routes
│ ├── core/
│ │ ├── ai_client.py # talks to the LLM
│ │ ├── cache.py # Redis helpers
│ │ ├── game_logic.py # linked list + validation
│ │ └── moderation.py # profanity / policy checks
│ ├── db/
│ │ ├── models.py
│ │ └── migrations/
│ └── main.py # app entry-point
├── frontend/
│ ├── index.html
│ ├── styles.css
│ └── app.js # minimal JS / React
├── tests/
│ └── e2e_duplicate_test.py
├── Dockerfile
├── docker-compose.yml
├── requirements.txt # or package.json if Node
└── README.md
Things to Keep in Mind
● Prompt <= 20 tokens whenever possible – keep it cheap.
● Handle LLM rate‑limits/back‑offs gracefully.
● Make sure the linked list can’t grow unbounded in RAM – persist or prune.
● Document how to reset the game state during local testing.
● Stick to semantic versioning and clear commit messages.
● Make sure the responses by LLM (Host) are creative and do not repeat in a
single run.
Contact
For any questions during the internship or to submit your deliverables, please reach out
to: Divyansh Sharma – Wasserstoff
Email: divyansh.sharma@thewasserstoff.com
