Practical guides on using AI tools like ChatGPT, GitHub Copilot in your workflow
104 articles
JSON mode and structured output features greatly improve AI integrations, but they don't eliminate malformed or unusable responses. Learn why structured output still fails, common causes, and proven techniques for building reliable LLM-powered applications.
A high precision-recall curve doesn't always translate into a successful machine learning model. Learn why models with excellent PR curves still fail in production, how evaluation metrics can mislead, and what additional analyses you should perform before deployment.
Cursor AI Chat and GitHub Copilot Chat are two leading AI coding assistants, but they excel in different scenarios. Compare their code understanding, project awareness, editing workflows, integrations, and developer experience to choose the best tool for each task.
System prompts define how AI assistants behave, but they aren't always as secret as developers assume. Learn what system prompt leakage is, why it happens, the risks it creates, and how to build AI applications that remain secure even if prompts become exposed.
Is your deep learning model performing well during training but failing during inference? Learn why Batch Normalization causes inconsistent predictions, how training and inference modes differ, and the best practices for building reliable production models.
Aider is an AI-powered pair programmer designed to work directly with your existing codebase. Learn how to use Aider to diagnose bugs, understand unfamiliar projects, generate targeted fixes, and collaborate safely without losing control of your code.
Function calling enables LLM agents to interact with APIs, databases, and external tools, but poorly formed arguments can cause unexpected failures. Learn why tool calls break, common causes of invalid parameters, and proven techniques for building reliable AI agents.
Many Cursor AI users struggle with inaccurate code suggestions because they're providing the wrong context. Learn the difference between Notepads and Context Files, when to use each, and how proper context management dramatically improves AI-generated code quality.
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