Practical guides on using AI tools like ChatGPT, GitHub Copilot in your workflow
96 articles
Your Retrieval-Augmented Generation (RAG) application performs well during testing but suddenly experiences latency spikes in production. The problem isn't always the LLM. Learn how to identify whether your bottleneck is the embedding service, vector database, document retrieval, network, reranking,
Your recommendation model performed exceptionally during offline testing, but click-through rates dropped after deployment. The culprit may not be the ranking model—it could be embedding drift. Learn what embedding drift is, why it happens, how it affects recommendation systems, and practical
Modern AI coding assistants can do more than generate code—they can help debug it. Cursor AI's Agent Mode enables iterative debugging by inspecting code, proposing fixes, updating multiple files, and responding to compiler or test feedback. Learn how to use Agent Mode effectively while keeping human
Bigger context windows don't automatically produce better AI responses. In many cases, stuffing an LLM with excessive conversation history, documents, or retrieved passages reduces answer quality, increases latency, and introduces distractions. Learn why context window bloat occurs and how to keep
Your machine learning model produced well-calibrated probability estimates yesterday, but after retraining, its confidence scores no longer match reality. Learn why model calibration often degrades after retraining and how to build ML pipelines that maintain trustworthy probability predictions.
AI coding assistants have become an essential part of modern software development. Two of the most popular options—Codeium and GitHub Copilot—offer intelligent code completion, chat assistance, and multi-language support, but they differ in pricing, ecosystem integration, customization
Large Language Models don't hallucinate randomly. Certain categories of questions consistently produce higher rates of fabricated facts, nonexistent citations, invented APIs, and incorrect reasoning. Understanding these hallucination hotspots helps developers design safer AI applications
Data augmentation is designed to improve model generalization, but poorly chosen transformations can reduce accuracy, introduce label noise, and create unrealistic training examples. Learn why augmentation sometimes hurts performance and how to build augmentation pipelines that improve—not degrade
Modern AI coding assistants are evolving from autocomplete tools into autonomous coding partners capable of editing multiple files simultaneously. Windsurf AI's Cascade and Cursor's Composer both promise project-wide code generation, but they approach multi-file workflows differently.
ChatGPT can sketch circuit breaker logic in seconds, but the code it produces often flaps — tripping and recovering so fast the pattern becomes useless. Here's how to prompt your way to production-grade breaker logic.
LLM agents often perform well during the first few interactions but begin forgetting important details as conversations grow longer. This phenomenon—often called multi-turn memory collapse—is usually caused by context limitations, summarization strategies, retrieval failures, or prompt design
ChatGPT-generated idempotency key logic often looks correct but silently breaks under retries, race conditions, or partial failures — costing you real money. Here's how to prompt it properly.