APIs are the connective tissue of modern products. This guide distills proven practices for API design, security, observability, and reliability—covering the most frequent questions and edge cases teams face in production. Examples use FastAPI and Pydantic v2, but the principles generalize to any stack.
New to APIs? This guide explains core concepts in clear language, then walks you through building a small FastAPI service with essential security and testing tips. When you’re ready for advanced patterns, read the companion: Designing Secure and Scalable APIs — A Comprehensive Guide.
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.
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.
Bi-encoder retrieval is fast but imprecise. Cross-encoder reranking improves top-k precision at the cost of some latency. Here’s when and how to add it.