Practice AI EngineeringThe Way Engineers Actually Work
Sharpen your problem-solving skills through hands-on AI challenges inspired by real engineering scenarios.
0/140 problems solved across all sets
NumPy Array Computing Labs
Vectorized thinking and matrix operations for numerical computing workflows.
Easy
8
Medium
8
Hard
4
Set summary
This set focuses on core numerical operations inspired by NumPy workloads: vector math, matrix transformations, normalization, and optimization-friendly primitives.
Progress: 0/20 solved
Pandas Data Wrangling Labs
Transform tabular records with joins, grouping, windows, and feature pipelines.
Easy
8
Medium
8
Hard
4
Set summary
This set covers practical tabular analytics patterns inspired by Pandas: filtering, joining, aggregating, window functions, cohort analytics, and lightweight feature engineering.
Progress: 0/20 solved
Python Core Problem Solving
String, array, graph, and dynamic programming drills in pure Python.
Easy
8
Medium
8
Hard
4
Set summary
This set builds Python problem-solving fluency with common interview and production coding patterns including stacks, sliding windows, graph traversal, and dynamic programming.
Progress: 0/20 solved
Scikit-Learn ML Foundations
Implement ML metrics, validation, and optimization mechanics from scratch.
Easy
8
Medium
8
Hard
4
Set summary
This set strengthens applied machine-learning intuition behind scikit-learn workflows by coding metrics, data splits, simple learners, and optimization updates without black-box APIs.
Progress: 0/20 solved
LLM Core Engineering Drills
Build production-grade prompt, output, and reliability logic in pure Python.
Easy
8
Medium
8
Hard
4
Set summary
This set focuses on the engineering primitives around LLM interactions: prompt formatting, output validation, safety filters, and orchestration basics.
Progress: 0/20 solved
AI Agents, Tool Use, and Guardrails
Code deterministic agent runtime primitives, policies, and supervision controls.
Easy
8
Medium
8
Hard
4
Set summary
This set covers tool routing, plan execution, policy enforcement, escalation logic, and run supervision for safe agentic systems.
Progress: 0/20 solved
RAG and Retrieval Systems Coding
Implement retrieval, ranking, and evaluation utilities used in real RAG stacks.
Easy
8
Medium
8
Hard
4
Set summary
This set trains chunking, retrieval ranking, citation checks, and retrieval metric evaluation for practical RAG system behavior.
Progress: 0/20 solved