If you are new to data science and artificial intelligence, understanding hallucinations in large language models (LLMs) like ChatGPT, GPT-4, or similar platforms is essential. Simply put, hallucination is when a language model generates an answer or text that sounds plausible, coherent, and confident but is actually factually incorrect or…
Prompt engineering is the skill of creating effective instructions for AI models. For developers and data scientists, it’s essential because the quality of an AI’s output depends entirely on the quality of the input prompt. The commonly used large language models (LLMs) in 2025 include ChatGPT (GPT-5), Claude 4, DeepSeek…
The Transformer architecture lies at the heart of today’s large language models (LLMs) like GPT-4, Claude, and Gemini, revolutionizing how machines understand and generate text. Introduced in the 2017 paper “Attention Is All You Need” by Vaswani et al., this architecture replaced older recurrent models by offering a faster, more context-aware approach to processing…
Imagine you’re reading a long story. Halfway through, you come across the word “she.” To know who “she” is, your brain quickly looks back at earlier sentences: “Oh yes, the girl with the red umbrella!” That ability to look around and find the right connection is exactly what makes transformers…
When you talk to an LLM, you type in words. But computers don’t understand words the way humans do. They only work with numbers. So, how does your sentence, “Good morning, how are you?” turn into something a machine can process? The answer is tokenization. Tokenization is the process of…
Large Language Models, or LLMs, are everywhere these days—chatbots, writing assistants, coding helpers, even customer support. But what exactly is an LLM? Every time you ask ChatGPT a question, you’re using an LLM. But how does it actually work? The easiest way to understand is to start simple. At its…
Time series forecasting is a statistical technique used to predict future values based on historically observed data points ordered by time. Widely used in finance, economics, and business, it helps stakeholders anticipate future trends and make informed decisions. A time series is a sequence of data points, measured typically at…
Triple Exponential Smoothing, commonly known as the Holt-Winters Method, extends upon Double Exponential Smoothing to address time series data that contains both a trend and a seasonal component. It incorporates three equations to capture the level, trend, and seasonality of a dataset, making it particularly useful for predicting values in…
Double Exponential Smoothing, also known as Holt’s Linear Exponential Smoothing, is a time series forecasting method that extends Simple Exponential Smoothing. While Simple Exponential Smoothing is best suited for time series without a trend, Double Exponential Smoothing can handle time series data with a trend but no seasonality. The primary…
Simple Exponential Smoothing (SES) is a time series forecasting method that is especially suitable for univariate data without a trend or seasonal pattern. It uses weighted averages of past observations to forecast future points. The method is ‘exponential’ because the weights decrease exponentially as observations get older. Key Concept: Smoothing…