Tag: GenAI

Small Language Models: Big Brains in Small Packages

For the last few years, the AI industry has behaved like a teenager at a gym: bigger, bigger, bigger. Every new language model arrived with more parameters, more data and a bigger electricity bill than the last. Then a quiet rebellion began. Researchers started asking a very Indian question: “Kitna deti hai?” How much performance do we actually get for every rupee of compute? The answer gave rise to Small Language Models, or SLMs. These are the compact, fuel-efficient cousins of giant models like GPT or Claude. They will not win a trivia contest against their big relatives, but they can run on your laptop, answer in a blink, and keep your data at home. In this post, let us open the bonnet and see what SLMs are, how they work, and why businesses are suddenly so fond of them.

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My Chatbot Has a Best Friend – Ways AI Tries to Feel Human

Imagine you open an app and an animated character you have been chatting with sends you a message: “So, my friend XXX was playing dodgeball in P.E. class, and while trying to dodge the ball, he tripped and fell. Oh gosh.” You chuckle. Poor XXX. You type back, “Is he okay?” and before you know it, you have exchanged twenty messages about a gym class that never happened, involving a boy who does not exist, told by a character who is entirely made of code.

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What is Prompt Engineering: An overview

Prompt engineering refers to the practice of designing and structuring inputs to large language models so that they produce accurate, useful, and reliable outputs. As language models have grown more capable, the way prompts are written has become an important skill, blending aspects of linguistics, logic, and problem formulation. Over time, several distinct types of prompt engineering have emerged, each suited to different tasks and levels of model guidance. The following discussion presents the main types of prompt engineering in an essay-style narrative, with an example woven into each explanation.

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