Image credit: hbr.org Here I covered a detailed and beginner friendly explanation of some of the most powerful prompting techniques. Zero Shot Prompting: In zero shot prompting, the model receives a direct instruction without any examples. It’s like asking a question to someone who’s never seen a similar one before and hoping they understand the […]
Image Credit: dataconomy When training a machine learning model, one of the most common question is, How many epochs should I run? The answer isn’t always straightforward. An epoch refers to one complete pass of the training dataset through the model. Running multiple epochs can bring big benefits, but also big risks if not handled […]
As AI becomes smarter, it is easy to assume machines can work entirely on their own. But in reality, even the best models need a human touch. That’s where Human in the Loop (HITL) comes in. It is a process where humans actively guide, review and fine tune the AI, especially in situations where data […]
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 […]
Image Credit:istockphoto Prompt injection is a growing concern in the world of AI. It happens when a user manipulates what they type into an AI system in a way that tricks the model into ignoring its original instructions. For example, if an AI is told, “You are a helpful assistant. Do not give advice on […]
Image credit: otalk9focus Reinforcement Learning (RL) is a core pillar of modern AI systems. It mimics how human learn by trying, receiving feedback, and improving over time. (To better understand RL, think of how dogs are trained with rewards and penalties.) Unlike supervised learning, where models learn from labeled datasets, RL allows AI to explore […]
Image credit: nature In AI, learning isn’t one-size-fits-all. Models learn from data in different ways, and understanding the three core types – supervised, unsupervised and deep learning (descriptive learning) – can help us better grasp how modern AI powers everyday applications, especially recommendation systems on platforms like Netflix and Amazon. Supervised learning is like training […]
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