Debabrata Pruseth

Debabrata Pruseth is an Enterprise Architect, Applied AI Leader, and Technology Strategist specializing in enterprise AI, generative AI, cloud strategy, and digital transformation. He helps organizations bridge enterprise strategy and practical AI implementation to create scalable and responsible business outcomes.

Iteration of Thought Prompting: Making AI Improve Its Own Thinking

Great answers don’t happen in one go—they improve over time.
Our new blog introduces Iteration of Thought prompting, a method that helps AI refine its responses through self-review and iteration.

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Logic of Thought Prompting: Making AI Reason Like a Logician

Ever felt AI answers sound convincing… but aren’t actually correct?
In our latest blog, we explore Logic of Thought prompting—a powerful technique that helps AI reason using facts, rules, and structured logic.
If you want more accurate and reliable AI responses, this is a must-read!

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Tree of Thought Prompting Explained: Make AI Think Smarter

Tree of Thought Prompting Explained: Make AI Think Smarter Read More »

Earth, in a Bottle. A Robot, with Imagination. And an AI That “Knows Everything.”

Earth Models, World Models, and Global Models are three fast-growing “families” of AI systems that are often mentioned together—but they solve very different problems. This blog provides an accessible, research-oriented map of the landscape for readers who are new to AI (and for technical readers entering the field).

Earth, in a Bottle. A Robot, with Imagination. And an AI That “Knows Everything.” Read More »

Deep Learning Concepts for Beginners : Cooking Pasta Analogy

What does learning deep learning have in common with cooking pasta?

More than you might think.

In this article, I explain deep learning concepts for beginners using the familiar process of learning to cook pasta—preparing ingredients, tasting, adjusting, and improving with every attempt. It’s a simple, intuitive way to understand how neural networks train, make mistakes, and get better over time—without heavy math or jargon.

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Deep Learning Concepts for Beginners : Basketball Player Analogy

What if understanding neural networks felt as natural as watching someone learn basketball?

In this short read, I explain how a neural network learns—using the simple, familiar journey of a player practicing shots, missing, adjusting, and improving. No heavy math. Just intuition, clarity, and a fresh way to see how AI actually learns.

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A Beginner-Friendly Guide to Privacy in AI

Privacy in AI

What happens when your AI model is accurate… but not private?
Trust disappears.
In this blog, I show you how to build privacy into your models using simple concepts like epsilon, clipping, and federated learning. We even run 60 privacy experiments to find the perfect balance. A great read for students and young professionals exploring responsible AI.

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A Beginner-Friendly Guide to Explainable AI (XAI)

Curious how AI models make decisions—or what goes on inside their “black box”?

In my latest blog, I break down explainable AI with hands-on Python examples, using real tools like SHAP, LIME, ELI5, and DALEX. Whether you’re a student, educator, or just passionate about responsible tech, this guide will help you see (and trust!) how machine learning models “show their work.”

Discover:
– What “explainability” really means
– How to interpret model explanations and plots
– Why transparency and fairness in AI matter

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How to Detect Hidden Bias in Your ML Model — A Step-by-Step Tutorial 

Ever wondered how AI decides who gets hired or approved for a loan?
Spoiler: it sometimes inherits our biases.
In my latest blog, I uncover how bias sneaks into machine learning—and how we can fix it to make AI fairer for everyone.

👉 Read the full story: The Hidden Bias in Machines

How to Detect Hidden Bias in Your ML Model — A Step-by-Step Tutorial  Read More »

A Beginner’s Guide to Time Series Modeling

Ever wondered how AI predicts tomorrow’s weather, stock prices, or even your heart rate? In my latest blog, I break down how machines learn from the past to predict the future — exploring trends, seasonality, and hidden patterns in data. You’ll discover the evolution from classic models like ARIMA and Random Forests to modern deep learning architectures such as LSTMs and Transformers, and even the rise of foundation models like TimeGPT and TimesFM. Perfect for beginners curious about how AI understands time and turns data into foresight.

A Beginner’s Guide to Time Series Modeling Read More »

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