Practice Platforms¶
Where to keep your skills sharp and find real LLM engineering challenges.
Kaggle¶
kaggle.com Machine learning competitions and datasets. For LLM engineering: - LLM Science Exam — RAG + reasoning challenge - LMSYS Chatbot Arena — preference data, model evaluation - PII Data Detection — text classification, NER
Best for: Getting hands-on with evaluation metrics and competing on a leaderboard.
Hugging Face¶
huggingface.co/spaces Deploy and explore ML demos. For practice: - Fork a Space and improve it - Publish your fine-tuned model and run inference - Check the leaderboards (Open LLM Leaderboard, MTEB) to understand model tradeoffs
Best for: Publishing your work and seeing what others have built.
LeetCode (for coding rounds)¶
LLM engineering interviews often include a coding component. Focus on: - Async/concurrent Python (threading, asyncio) - String processing and parsing (tokenization, chunking) - Data structures for caching (dict with TTL, LRU cache) - Graph traversal (relevant if interviewing for LangGraph-heavy roles)
Open source contributions¶
Contributing to LLM libraries is the fastest way to signal credibility:
LangChain — github.com/langchain-ai/langchain - Good first issues labeled. Focus on: adding new retrievers, fixing documentation, adding examples.
LangGraph — github.com/langchain-ai/langgraph - Smaller codebase, easier to understand fully.
ChromaDB — github.com/chroma-core/chroma - Vector database. Python and Rust. Good for systems-minded contributors.
RAGAS — github.com/explodinggradients/ragas - Evaluation library. Good for those interested in LLM evaluation research.
AI safety and alignment¶
ARC Evals / METR challenges — metr.org Model evaluation tasks requiring tool use, planning, and multi-step reasoning. Best for: Understanding agent capabilities and failure modes.
Building in public¶
The most effective practice: build something real, document it, and share it. Options: - Write a detailed blog post on Medium or Substack about something you built - Publish your capstone project on GitHub with a clear README and eval results - Post a 3-minute demo video on LinkedIn or Twitter showing what you built - Answer LLM engineering questions on Stack Overflow
Building in public creates accountability, generates feedback, and builds your network — all of which compound over time.