Plagiarism Checker Design: Key Algorithms and Interview Prep

Understanding how to detect duplicate content and measure document similarity is a classic software engineering challenge frequently tested in technical interviews. This text-only course guides you through the fundamental algorithms and system design principles behind a scalable plagiarism checker. You will learn how to break down complex string-matching problems, analyze document similarity, and apply these exact problem-solving techniques to top coding interview questions. What you'll learn: - Understand core string-matching algorithms, including Rabin-Karp, Knuth-Morris-Pratt, and Naive search. - Apply document similarity metrics like Jaccard similarity, Cosine similarity, and modern vector embeddings to compare text. - Design scalable system architectures capable of processing and indexing large volumes of documents efficiently. - Implement hashing techniques like MinHash and Locality-Sensitive Hashing (LSH) to detect near-duplicates at scale. - Analyze how these architectural patterns and algorithms map directly to common coding interview questions. - Practice solving algorithmic challenges through structured written exercises and conceptual walkthroughs. You will start with the foundational definitions of string similarity before moving into advanced hashing techniques and system design trade-offs. The course concludes with a dedicated focus on adapting these concepts to clear your technical interviews. This course is designed for beginner to intermediate software engineers, computer science students, and anyone preparing for technical system design or coding interviews. No advanced system design experience is required. Start reading today to master the algorithmic foundations of document comparison and elevate your interview readiness.