Image credit:msp-channel As organizations race to adopt AI solutions, one question stands out: Is our data actually ready for AI? Does high quality data automatically translate to AI readiness? That’s still questionable. Making data AI ready goes beyond cleanliness or completeness. It’s about aligning data to a specific use case and ensuring it reflects real […]
Image credit: toptal We have abundant raw data in today’s AI driven world, but it’s meaningless to machines without context. That’s where data labeling becomes critical. It’s the process of tagging raw data like images, text, audio, or video, with meaningful annotations that help AI models learn. Think of data labeling as giving the answers […]
In AI model fine tuning, data is only half the story. The other half lies in how we guide the learning process, through hyperparameters. These are high level configuration setting that control the training process, like how fast a model learns(learning rate), how many times it sees the data( epochs), and how much data it […]
Image credit: bdtechtalks In every interaction with an AI model, there’s a limit to how much information it can process at once. This is known as the context window, and it includes both the prompt you provide and the response the model generates. The model doesn’t just read the input and reply, it analyzes token […]
In today’s AI-powered development landscape, a fresh concept is reshaping recently how we write code – vibe coding. Unlike traditional development that demands strict syntax and technical fluency, vibe coding allows developers, testers, and product teams to collaborate with AI using natural language. You describe your intent by writing simple prompts, for example, “Generate test […]