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.

LLMOps (Large Language Model Operations)

Volcano Engine Reinforcement Learning for LLMs

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.

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

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

Beyond Single Agents - The Rise of Collaborative AI

When Chains Aren't Enough

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

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

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.

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

LoRa: Low-Rank Adaptation

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

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.

Fast Inference engine with low latency

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.

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

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

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.

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.

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.

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.

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

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

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.

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.

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.

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.

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.

10 Lessons from the book