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Latest posts
Embeddings Explained: How Machines Turn Meaning Into Numbers
The core idea that makes semantic search and RAG possible — how text becomes vectors, what similarity actually means in that space, and how to evaluate whether your embeddings are any good.
Natural Language ProcessingEmbedding Models: From Word2Vec to Voyage AI — A Practical Field Guide
A grounded tour of the embedding models that actually matter — how we got from static word vectors to today's Voyage, OpenAI, and BGE-class models, and a framework for choosing between them.
Natural Language ProcessingInformation Retrieval Fundamentals: The Theory Every RAG System Is Built On
Before you build a retriever, understand what it's actually doing. A grounded walkthrough of IR fundamentals — relevance, precision, recall, TF-IDF, BM25, and the lexical-vs-semantic divide.
Natural Language ProcessingIntroduction to RAG: What It Is, Why It Was Invented, and How It Fits the LLM Landscape
A foundational walkthrough of Retrieval-Augmented Generation — what it is, why standalone LLMs need it, and how it compares to fine-tuning and search.
PythonPolymorphism in Python: Method Overriding, Duck Typing, Operator Overloading, and Runtime Dispatch
Learn Python polymorphism with practical examples covering method overriding, runtime method resolution, duck typing, operator overloading with magic methods, and object-oriented programming (OOP) best practices for writing flexible, scalable applications.