A practical guide for software engineers on building production-grade RAG systems using hybrid retrieval, re-ranking, and grounding techniques to reduce hallucinations and improve answer quality.

A practical deep-dive into vector embeddings and cosine similarity — the mathematical foundation that makes retrieval in RAG systems actually work.

A practical guide for software engineers covering how vector databases use Approximate Nearest Neighbor algorithms to search millions of embeddings efficiently, and how to chunk documents intelligently so your RAG pipeline actually retrieves useful, precise context.

A practical walkthrough of adding per-page context awareness to a floating AI chat widget built with Hugo and Netlify Functions, covering layout overrides, slug injection, priority chunk labeling, and the prompt engineering fix that made summarise-this-post actually work.

A practical walkthrough of building a conversational AI assistant for a Hugo static site using TF-IDF retrieval over a flat JSON knowledge base — no vector database, no backend server, no embeddings infrastructure required.

This article breaks down the key mathematical concepts behind neural networks, including weights, biases, and activations, with an example of handwritten digit recognition.

Take a detour before diving into transformers and explore sub-word tokenization techniques like Byte-Pair Encoding, WordPiece, and Unigram models. Learn how they handle rare words, reduce vocabulary size, and make models more efficient!

Decoding N-Grams: The Heart of Large Language Models

Why am I writing these AI blogs? Discover my journey into AI and LLMs!