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AI engineering, explained simply
Guides on AI engineering, machine learning and deep learning: what the terms mean, what to learn, and in what order.
System Design Explained: HLD vs LLD with Examples
System design in plain English: what high-level design (HLD) and low-level design (LLD) are, how they differ, and a step-by-step method for each, with examples.
System Design Interview Questions: 20 HLD and LLD Questions with Answers
The 20 most common system design interview questions, split into HLD and LLD, with the key idea behind each answer and a structure for answering any of them.
UML Class Diagram and Sequence Diagram Explained with Examples
How to read and draw a UML class diagram and a sequence diagram: the class box, the six relationship arrows, multiplicity, and a step-by-step example.
Generative AI Course for Beginners: What to Learn, in Order
A generative AI course for beginners should follow a clear order. See the topics to learn, from how LLMs work to RAG, agents and evaluation, step by step.
What Is AI Engineering? What an AI Engineer Does Day to Day
AI engineering explained in plain English: what the field is, what an AI engineer does each day, the skills involved, and how it differs from ML work.
Prompt Engineering Guide: Techniques That Actually Work
A practical prompt engineering guide: how to write clear prompts, when to use few-shot examples, chain of thought and prompt chaining, and how to test them.
Machine Learning Course for Beginners: The Syllabus to Expect
What a machine learning course for beginners should teach: the full syllabus topic by topic, the math and Python you need, and how to judge a course.
LLM Evaluation: How to Test an AI Application Properly
LLM evaluation explained: how to build a test set, choose metrics, use an LLM as a judge with care, and evaluate RAG systems and agents before launch.
Deep Learning Course: What a Good Syllabus Should Cover
What a deep learning course should cover: neural networks, backpropagation, loss, regularization, RNNs, Transformers and PyTorch, plus how to judge a syllabus.
How to Become an AI Engineer: A Step-by-Step Roadmap
A clear roadmap to become an AI engineer: what to learn first, the order that works, how long each step takes, and what to build along the way.
Fine-Tuning an LLM: When to Do It and How It Works (LoRA)
Fine-tuning an LLM explained: what it changes, when it is worth doing, the steps from data to evaluation, and how LoRA and QLoRA make it affordable.
What Is a Large Language Model (LLM)? Explained Simply
What a large language model (LLM) is, in simple words: what it does, how it is trained, what it can and cannot do, and the main types you will meet.
AI Engineer vs Software Engineer: How to Move into AI
AI engineer vs software engineer: how the two roles differ, which skills carry over, what you must add, and a step-by-step plan to move from software to AI.
RAG vs Fine-Tuning vs Prompt Engineering: Which to Use When
RAG vs fine-tuning vs prompt engineering: what each one changes, what it costs, a simple way to choose between them, and how to combine all three.
Transformer Architecture Explained Step by Step
Transformer architecture explained step by step: tokens, embeddings, positions, self-attention, feed-forward layers, and encoder vs decoder models.
What Is RAG? Retrieval-Augmented Generation and How It Works
What RAG (retrieval-augmented generation) is and how it works: indexing, retrieval and generation step by step, why RAG fails, and how to improve it.
AI Engineer vs ML Engineer vs Data Scientist: Differences
AI engineer vs ML engineer vs data scientist: what each role builds, the skills and tools each one uses, where they overlap, and how to choose a path.
AI Engineer Projects for Your Portfolio: Ideas by Level
AI engineer project ideas for your portfolio, grouped by level: beginner, intermediate and advanced, with what each one teaches and how to present it.
Vector Databases and Embeddings Explained for Beginners
Vector databases and embeddings explained simply: what an embedding is, how similarity search works, what HNSW and ANN mean, and when you need a vector store.
What Are AI Agents and How Do You Build One? A Clear Guide
What AI agents are, how the agent loop works, the parts every agent needs (model, tools, memory, planning), and the steps to build a simple one yourself.
How to Choose an AI Engineering Course: What It Must Cover
How to choose an AI engineering course: the topics a good one must cover, the signs of a weak syllabus, and a checklist to compare courses before you pay.
Machine Learning vs Deep Learning vs Generative AI Explained
Machine learning vs deep learning vs generative AI: how the three terms fit inside each other, what each is good at, with examples and when to use which.
AI Engineer Interview Questions and How to Prepare for Them
Common AI engineer interview questions on LLMs, RAG, agents, evaluation and system design, with what interviewers look for and a plan to prepare.
How to Learn Machine Learning from Scratch: A Beginner Path
How to learn machine learning from scratch: the math you need, the concepts to study in order, projects to build, and the mistakes beginners should avoid.
Deep Learning Explained: Neural Networks to Transformers
Deep learning explained for beginners: what a neural network is, how it learns with backpropagation, and how the field moved from RNNs to Transformers.
AI Engineer Skills: What to Learn and in What Order
The skills every AI engineer needs, grouped into six areas, with the order to learn them and a simple way to check whether you really know each one.
How LLMs Work: Tokens, Embeddings and Attention Explained
How LLMs work in plain English: how text becomes tokens, tokens become embeddings, attention adds context, and the model writes one token at a time.