This guide gives you a field‑tested taxonomy of RAG types, the key techniques behind them, and a decision framework for when to use what—plus implementation recipes you can adapt today.

The Practical Guide to RAG: Types, Techniques, and How (and When) to Use Each

Guide to “RAY” by Anyscale

8-Stage Lifecycle of Modern LLM Applications

Volcano Engine Reinforcement Learning for LLMs

“Verl" for LLM Reinforcement Learning (Beyond Pre-training)

OLMo isn't just another model; it's a scientific artifact. It's built on a philosophy of being "fully open," providing the entire recipe, not just the finished cake.

OLMo

DeepSpeed is an open-source deep learning optimization library for PyTorch, created by Microsoft.

DeepSpeed

Unsloth is an open-source AI library designed to significantly speed up LLM finetuning and reduce memory usage.

Finetune LLMs 2-5x Faster: An In-Depth Guide to Unsloth

Beyond Single Agents - The Rise of Collaborative AI

A Deep Dive into CrewAI for Collaborative AI Systems

Beyond Linear Chains: A Deep Dive into “LangGraph” for Building Stateful AI Agents

Evaluating a large language model (LLM) isn’t one metric—it’s a multi‑dimensional scorecard. This guide shows you how to build a practical, repeatable evaluation for accuracy, performance, and latency

How to Evaluate an LLM (Accuracy, Performance & Latency)

RAG systems aren't foolproof—they can suffer from poor retrieval of irrelevant documents or hallucinations in generated outputs.

Evaluate RAG

Reinforcement Learning from Human Feedback (RLHF) is a technique that uses human preferences as a guide for training models – essentially using reinforcement learning (RL) to directly optimize a model’s behavior based on what humans find desirable.

Reinforcement Learning from Human Feedback (RLHF)

LangChain is a powerful framework for building applications powered by LLMs.

A Beginner's Guide to LangChain

The Deep Dive into LoRA

NIM is a pre-built, optimized, and containerized AI model that you can run with a single command.

NVIDIA Inference Microservices (NIMs)

NVIDIA Triton Streamlines Your Path to Production

This foundational section establishes the historical and conceptual context for CUDA, explaining why it was created and the fundamental problem it solves. We will move from the general concept of GPU computing to the specific architecture of a CUDA-powered application.

The CUDA: From Foundational Principles to High-Performance Parallel Computing

SGLang: The Engine That's Redefining High-Performance LLM Programming

The conceptual innovations of PagedAttention and Continuous Batching are realized through a well-engineered, modular software architecture designed for performance, extensibility, and production readiness.19 Understanding the key components of the vLLM engine reveals a system built not just for speed, but also for scalability and maintainability.

Deep Dive into vLLM

Synthetic data helps you move faster, protect privacy, balance classes, and stress-test edge cases.

Generate High-Quality Synthetic Data 📊 for ML/DL & GenAI Projects

LegalLLM, a multi-task Large Language Model designed specifically for the complexities of U.S. legal analytics.

LegalLLM 👩🏼‍💼: Revolutionizing Legal Analytics with AI

Deep Dive into NVIDIA TensorRT with PyTorch and ONNX

We’ll cover requirements, architecture, scaling challenges, data modeling, caching, APIs, and more — with diagrams to keep things visual.

System Design Interview: Design Instagram 📸 

Visual Studio Code (VS Code) has become one of the most popular code editors thanks to its powerful features and extensibility. However, mastering VS Code goes beyond installing extensions and tweaking settings — it’s about efficiency. Keyboard shortcuts can dramatically speed up your development workflow, reduce context switching, and keep your hands on the keyboard. This post covers essential VS Code shortcuts every developer should know, organized by categories and tailored for both Windows/Linux and macOS users.

VS Code Keyboard Shortcuts Every Developer Should Know

Understanding algorithmic complexity is crucial for writing efficient code and making informed decisions about data structures and algorithms. This cheat sheet covers the fundamentals of Big O notation, common time and space complexities, and real-world examples with code snippets.

Big O Cheat Sheet

Time series forecasting is a cornerstone of data science, enabling practitioners to predict future values based on historically observed data points. From stock prices and weather patterns to website traffic and sensor readings, accurate forecasts inform strategic decisions across industries. In this post, we'll explore a range of methods—from classic statistical techniques to modern machine learning and deep learning approaches—and demonstrate how to implement them in Python.

Time Series Forecasting Methods in Data Science

PyCaret is an open-source, low-code machine learning library in Python that automates and streamlines the entire ML workflow

PyCaret Guide

Scraping Images from the Web Using Selenium. It covers everything from environment setup to running a robust scraper, with code snippets and best practices.

Scraping Images from the Web Using Selenium

System design interviews assess your ability to architect large-scale services. Unlike algorithm questions, they focus on high-level thinking: gathering requirements, making trade-offs, and balancing non-functional needs (scale, reliability, maintainability). In this guide you’ll learn a repeatable framework, key building blocks, common patterns, and a worked example.

System Design: Complete Guide for Interviews

In supervised-learning, unsupervised-learning, time-series, deep-learning and reinforcement-learning tasks, each modeling problem brings its own “sweet-spot” of algorithms, solvers/optimizers, and hyperparameter defaults. Below is a practical guide to choosing models, optimizers (or solvers), learning‐rate heuristics and when to reach for each technique.

The Ultimate Cheat Sheet: Picking the Right Model, Optimizer & LR for Every Scenario

GitHub has become the de facto platform for hosting, collaborating on, and managing software projects. Whether you’re an individual developer or part of a large team, understanding how to leverage GitHub effectively can dramatically improve your workflow. In this “Complete Guide,” we’ll walk through everything from the basics of Git and repositories, to advanced features like pull requests, GitHub Actions, and best practices for collaboration.

Github Complete Guide

Web scraping is the automated process of extracting information from websites. While simple HTTP requests and HTML parsing libraries (like requests and BeautifulSoup) work for many static sites, dynamic pages driven by JavaScript require a browser-like environment. That’s where Selenium comes in: a powerful browser-automation tool that can drive a real (or headless) browser to render pages, interact with elements, and retrieve the fully generated HTML.

Web Scraping using Selenium guide

Streamlit is a Python library for building web apps. Running apps locally depends on your system’s configuration and compatibility, which can sometimes be a hurdle.

How to run Streamlit in google colab