Hallucinations in LLMs aren’t an arcane glitch, they’re a byproduct of how we measure and reward model behavior. When accuracy dominates and calibrated uncertainty isn’t valued, systems learn to guess rather than pause.

For years, talking to AI has been somewhat akin to talking to Finding Nemo's Dory, friendly, intelligent, but forgetful. Each dialogue started from scratch, regardless of the conversational duration. Now, the future is not so much bigger models or faster turnaround; it's memory.

Too often we expect AI to understand us perfectly with minimal effort, but the secret lies in the prompt.

In recent months, language models (LLMs) have gone from being a technological curiosity to becoming key tools for businesses and professionals.

Generative AI, especially large language models like ChatGPT, Gemini, or Claude, is revolutionizing the way we work, collaborate, and build solutions.

This week, we’re wrapping up with a summary that serves as a reminder of everything we’ve explored together.

AI is typically described as this giant brain, a single, all-knowing system that gives an answer to whatever you ask it. But imagine a smarter way?

Last week, we explored how AI is changing the way we think about building, making it easier than ever to turn ideas into action.

How does AI really understand what we ask it? And how is it able to retrieve relevant information even if we don't use the same words?

Take a look at this week's article, which breaks it down in simple terms, what AI agents are, how they work and why they're a lot more than chatbots.

Here, we delve into the world of fine-tuning AI what it is, how it works and why it's the answer to training big language models to perform a specific task, be it talking in the voice of your company or handling specialized content.

How do you program a computer to respond accurately to questions about things it's never even seen before?

Under the hood is something much bigger, learnable models (almost) for all.

In this article, I explore the fundamental layers of AI, from large concepts to small concepts by discussing Artificial Intelligence, then how it relates to strategic topics like Machine Learning, then Deep Learning and finally Neural Networks.

New article exploring the power behind every AI response and why inference — not just training — is the real energy challenge. Includes two working Groq examples and a Reflex-based web app to collect learnings.

New article on real-world AI in asset management with two examples anyone can follow using simple tools like Google Colab and Hugging Face.

"AI for the Layman" is a series of articles that explain artificial intelligence in simple terms, without jargon, using everyday examples.