RAG Explained: Build a Retrieval-Augmented Generation App with Python
Learn how Retrieval-Augmented Generation (RAG) works, why it outperforms basic LLMs for custom data, and how to build a working RAG app in Python.
Math · ML · Data Science
Understand the mathematics behind the models.
Xray AI Lab publishes tutorials and notes on mathematics, Python, data science, machine learning and deep learning. Each article starts from the underlying math and ends with code you can run.
- AI Agents Explained: How to Build an LLM Agent with Python25 October 2026
- How to Evaluate LLMs: A Practical Guide to Testing AI Responses25 October 2026
- Backpropagation Explained: How Neural Networks Learn24 October 2026
- Data Cleaning with Python and Pandas: A Complete Practical Guide24 October 2026
- Data Quality in Data Science: How to Detect and Fix Bad Data with Python24 October 2026
- Exploratory Data Analysis (EDA) with Python: A Complete Project24 October 2026
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Tools & Tutorials
12 min
AI Agents Explained: How They Work, Tools, and Real-World Examples in 2026
Learn how AI agents work, explore real-world applications, compare top frameworks, and build a simple AI agent with Python.
Machine Learning
20 min
Mathematics for Machine Learning: A Complete Study Map
A beginner-friendly roadmap to the mathematics behind machine learning: linear algebra, geometry, calculus, probability, and optimization, and how they connect.
Latest articles
Recently published notes on math, code and models.
NLP & Generative AI
10 min
AI Agents Explained: How to Build an LLM Agent with Python
AI agents explained: learn how LLM agents use tools, state, and a loop, then build a safe local agent in Python with Ollama, tests, and security guardrails.
NLP & Generative AI
12 min
How to Evaluate LLMs: A Practical Guide to Testing AI Responses
Learn how to evaluate LLM responses with test sets, code-based graders, human review, and LLM-as-a-judge, plus a runnable Python harness and common pitfalls.
Deep Learning
15 min
Backpropagation Explained: How Neural Networks Learn
Learn how backpropagation works: the chain rule, a fully worked numeric example, a NumPy implementation with gradient checking, and common pitfalls.
Data Science
10 min
Data Cleaning with Python and Pandas: A Complete Practical Guide
Learn a repeatable pandas data cleaning workflow: audit, fix text and types, parse dates, handle invalid values, remove duplicates, flag outliers, and validate.
Data Science
12 min
Data Quality in Data Science: How to Detect and Fix Bad Data with Python
Learn to measure data quality across six dimensions, detect and fix bad data with pandas, enforce rules with pandera, and see how bad data hurts models.
Data Science
11 min
Exploratory Data Analysis (EDA) with Python: A Complete Project
Run a complete exploratory data analysis in Python with pandas and seaborn on the Palmer Penguins data, from data quality checks to a Simpson's paradox.
Mathematics
13 min
Probability for Machine Learning: Bayes & Distributions
Learn the probability behind machine learning: Bayes' theorem, key distributions, MLE and MAP, with worked examples and Python code you can run and check.
Research Notes
15 min
Agentic AI in 2026: What Research Reveals About Autonomous Agents
Agentic AI explained through research: agent architectures, planning, tools, memory, what autonomous agents can really do in 2026, and where they still fail.
Mathematics
15 min
Calculus for Machine Learning: Derivatives, Partial Derivatives, and Gradients
Learn derivatives visually, then see how partial derivatives and gradients drive neural-network training, with worked examples and verified Python code.
Computer Vision
11 min
Computer Vision Explained: Tasks, Models, and Applications
What is computer vision? Learn the core tasks, the key models from CNNs to Vision Transformers, real applications, and runnable Python examples.
Mathematics
10 min
Gradient Descent Explained with Python: Math, Visualization, and Code
Learn how gradient descent minimizes a loss function. Derive the gradients, code it in NumPy, and test learning rates with a plotted experiment.
Mathematics
10 min
Linear Algebra for Machine Learning: Vectors, Matrices, and Eigenvalues (with NumPy)
Learn linear algebra for machine learning: vectors, dot products, matrix multiplication, and eigenvalues, with runnable NumPy examples and PCA.
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Python
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4Data cleaning, statistics, experiment design and honest evaluation.
Machine Learning
2Classical learning algorithms, derived and implemented from scratch.
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3Neural networks, backpropagation and modern architectures.
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4Language models, embeddings, transformers and generative methods.
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2Practical guides to the tools of the trade: Git, notebooks, environments.
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About the author
Nbark Kazdabi | Xray 0xFF
Master’s Student in Artificial Intelligence and Data Science
I am a Master’s student in Artificial Intelligence and Data Science with a background in Mathematics and Computer Science. Through this blog, I share practical tutorials, technical explanations, and lessons from my learning journey.