Writing clean, readable code is essential for collaboration and maintainability. Linters and formatters help us keep our codebase consistent and easy to understand.

Designing and evolving system architecture is about making informed trade‑offs. This guide provides a practical, opinionated walkthrough of the core concepts, patterns, and decisions you need to build scalable, reliable, and cost‑efficient systems—plus answers to the most common questions engineers and architects ask.

Missing values are inevitable in real-world datasets. This guide covers proven methods to handle missing data in pandas without compromising data integrity or analytical accuracy.

Document summarization is a critical NLP task that helps users quickly grasp key information from long documents. But how do you know if your model is actually working? This guide shows a workflow that starts with evaluation and acceptance criteria before touching models.

Product catalogs rarely match 1:1. I combine lexical and semantic similarity with thresholds to minimize false matches.

RAG is a design pattern, not a product. LangChain supports it out of the box. This guide shows a production-ready RAG setup in LangChain with architecture, retrieval choices, runnable code, evaluation metrics, and trade-offs from my client projects.

LightRAG is a minimal RAG toolkit that strips away heavy abstractions. Here’s a complete build with code, performance numbers versus a LangChain baseline, and when LightRAG is the right choice.

NumPy’s reshape() and flatten() are both used for array manipulation, but they serve different purposes and have distinct behaviors. This guide explains when and how to use each method effectively.

Vector search misses keyword-heavy queries. BM25 misses semantic similarity. Combine both with hybrid search for better retrieval recall.

Want LightRAG’s lean retrieval with LangChain’s chain ecosystem? Here’s how to wrap LightRAG as a LangChain-compatible retriever.