Vector Databases

Python code and mathematical formulas representing vector indexing.

Building an IVF-PQ Vector Index from Scratch in Pure Python

Learn how to build an IVF-PQ vector index from scratch using pure Python, implementing k-means clustering and product quantization to achieve scalable similarity search.

A conceptual diagram of a RAG pipeline with document ingestion, vector store, retriever, re-ranker, and LLM generator stages.

Building a Production-Grade RAG Pipeline: A Hands-On Portfolio Project That Signals Real Systems Skill

Move beyond the generic FAISS + LangChain + OpenAI tutorial. This guide walks you through building a multi-stage RAG pipeline with hybrid retrieval, cross-encoder re-ranking, and automated evaluation — the kind of project that signals real systems skill on a CV.

Building a Production-Grade Semantic Search Engine from Scratch

Learn how to build a scalable semantic search pipeline from scratch. This guide covers embedding generation, vector indexing, and quantization to create a portfolio project that signals senior-level systems proficiency.

Diagram of an LLM production system showing evaluation, retrieval, guardrails, and observability layers around a central model gateway.

LLMOps and GenAIOps: Production Patterns for Reliable LLM Systems

A working engineer’s guide to LLMOps and GenAIOps: the pipelines, evaluations, guardrails, and observability patterns that turn LLM prototypes into reliable production services.

Diagram of a RAG ingestion pipeline showing document parsing, chunking, embedding, and indexing stages.

The Hardest RAG Ingestion Problems and How Engineers Actually Solve Them

Most RAG failures start long before the LLM is ever called. This post walks through the five hardest ingestion problems and the patterns teams use to fix them in production.