Artificial intelligence has moved from research labs into everyday tools. This hub covers how modern AI actually works — the machine learning foundations, neural networks, and the large language models (LLMs) that power today's assistants: how they are trained, what they can reliably do, and where they fall short.
AI & TechnologyComparison
Semantic search understands meaning; keyword search matches words. The research shows neither wins outright — and the reason explains why production systems run both.
AI & TechnologyGuide
Embeddings turn text into lists of numbers so that similar meanings end up close together. Here is how that works, what it unlocked, and where it still fails.
AI & TechnologyComparison
They are not three ways to do the same thing — they fix three different failures. A diagnostic guide to choosing between them, what the head-to-head study really showed, and why 2026 narrowed the case for fine-tuning.
AI & TechnologyAnalysis
Every model launch leads with benchmark scores. Three separate failures make those numbers less meaningful than they look — including the finding that 57% of questions in one MMLU subject contain errors.
AI & TechnologyGuide
A context window is how much an AI model can hold in mind at once — and it is not memory. What tokens are, how big windows got, why a bigger one isn't automatically better, and what actually happens when a conversation runs out of room.
AI & TechnologyComparison
Vector search finds the passage that resembles your question. Some questions have no such passage. Here's the class of question GraphRAG exists for — and when it isn't worth the cost.
AI & TechnologyGuide
An AI agent isn't a smarter model — it's a plain loop wrapped around an ordinary request. Here's the mechanism, the six ways a turn can end, and why the bill grows faster than the conversation.
AI & TechnologyGuide
MCP is the USB-C of AI tooling — one standard so every app can talk to every tool. Here's what it actually defines, why it spread so fast, and the security gap that came with it.
AI & TechnologyGuide
A complete, beginner-friendly map of modern artificial intelligence — how machine learning, deep learning, large language models, RAG, and AI agents all fit together, and where each one is used.
AI & TechnologyGuide
AI hallucinations are confident, false answers that chatbots invent when they don't know something. This guide explains why they happen, shows a real courtroom example, and covers proven ways to reduce them.
AI & TechnologyGuide
Machine learning is how computers learn from examples instead of being told every rule. This beginner's guide explains how it works, the three main types, and where you already use it every day.
AI & TechnologyGuide
How multi-agent AI systems work: the orchestrator-worker pattern, why they outperform a single agent, real token-cost tradeoffs, and when to actually use one.
Artificial intelligence (AI) is the field of building computer systems that perform tasks normally requiring human intelligence — recognising images, understanding language, making predictions, and generating text. Most modern AI is built on machine learning, where systems learn patterns from data rather than following hand-written rules. Large language models such as GPT and Claude are a recent, powerful branch of AI focused on understanding and generating human language.
A large language model (LLM) is an AI system trained on massive amounts of text to understand and generate human language. It works by repeatedly predicting the most likely next piece of text, which lets it answer questions, write, summarise, translate, and generate code. Examples include GPT, Claude, and Gemini.
Training happens in stages. Pretraining exposes the model to enormous text datasets so it learns the patterns of language. Fine-tuning then adapts it to specific tasks, and reinforcement learning from human feedback (RLHF) aligns its responses with human preferences for helpfulness, accuracy, and safety.
Because an LLM generates text by predicting likely words rather than looking up verified facts, it can produce fluent statements that are wrong — often called hallucination. The risk is reduced, though not eliminated, by better training data, retrieval tools that ground answers in real sources, and careful prompting.