LLM : Large Language Model A computer model trained on massive amounts of text to predict the next word in a sentence. It doesn’t “think”, it generates language based on patterns. It sounds smart, but it’s just very good at gue... AI LLM
Tokens : How AI Measures Your Words Tokens are chunks of text, not always full words, but pieces of them. For example, “cat” is one token, but “unbelievable” might be two. LLMs read and generate language one token at a time. More tokens...
Training vs. Inference: Why Your AI Doesn’t Learn from Chat Training is when the model learns, it reads tons of text and adjusts itself to get better at predicting words. It’s slow, expensive, and happens just once in a while. Inference is when the model respo...
Why RAG is a Game-Changer for AI Answers RAG is how AI stops making stuff up and starts citing real facts. Instead of guessing answers from memory, RAG lets your AI look things up in documents, databases, or websites before it replies....
How AI Understands Language Through Math Embeddings are how AI turns words into math and meaning into maps. Instead of reading language like we do, AI converts text into vectors, long lists of numbers that capture meaning. This makes languag...
Vector Database : AI’s Search Engine for Meaning Vector database stores information as high-dimensional vectors (a bunch of numbers), so when you ask a question, it compares the vibes of your query with stored content and pulls out the closest match...
AI Agents : From Talking to Taking Action AI Agents are AI systems that don't just chat, they actually do things. Give them a goal like "plan my vacation," and they'll research, compare, and book everything without constant hand-holding. More...
MCP : How AI Talks to Tools Without the Chaos MCP is how AI connects to real-world tools without chaos. Instead of giving your AI custom code for every little task, MCP lets it talk to databases, APIs, and files through one universal protocol. Th...