Semantic Search & Vectors
An interactive visual guide to embeddings, cosine similarity, vector retrieval, and how semantic search works in practice.
Open articleBlog
Current & Upcoming Blogs
Practical writeups on search systems, vector databases, retrieval quality, reranking, and applied AI engineering workflows.
Published
BM25 vs Normal Search Explained
Why counting words is a bad idea — how BM25 judges quality over quantity with Python code, visual examples, and real-world analogies for beginners.
Open articleUpcoming
Vector DB Fundamentals
A practical guide to storing embeddings, indexing vectors, similarity search, and tradeoffs in vector database design.
Job Description vs Resume Analysis
Building a resume matching workflow that includes retrieval, reranking, gap analysis, scoring, and final recommendation output.
Hybrid Search: BM25 + Vectors
Combining lexical search and dense retrieval for stronger recall, then improving precision with reranking.