Updated June 2026 · 7 min read · No jargon, just clarity
Generative AI is artificial intelligence that creates new things — text, images, videos, code, music. When you ask ChatGPT a question and it writes an answer, that answer didn't exist before. The AI generated it. That's the "generative" part.
Before generative AI, most AI was "analytical" — it could classify emails as spam, recognize faces in photos, or recommend Netflix shows. But it couldn't create a new email, paint a new face, or write a new show script. Generative AI changed that.
LLM stands for Large Language Model. Think of it as a very advanced autocomplete. When you type a text message and your phone suggests the next word — that's a tiny language model. An LLM does the same thing, but with trillions of learned patterns from reading most of the internet.
Analogy: Imagine someone who has read every book, article, and website ever written. They can't look things up — they don't have the books in front of them — but they absorbed so many patterns that they can write convincingly about almost any topic. That's roughly what an LLM does.
Transformer: The architecture (blueprint) that all modern AI models use. Invented by Google in 2017. The "T" in GPT stands for Transformer.
Token: How AI reads text. One token is roughly ¾ of a word. "I love pizza" is about 3 tokens. AI models charge by tokens — so shorter messages cost less.
Prompt: Whatever you type into the AI. "Write me a poem about cats" is a prompt. Better prompts = better outputs.
Hallucination: When AI confidently says something that's completely wrong. It happens because AI generates text based on patterns, not facts. Always verify important information.
Temperature: A setting that controls creativity. Low temperature = predictable, focused answers. High temperature = creative, varied (but potentially wilder) answers.
Context window: How much text the AI can "see" at once. A 1M token context window means the AI can read and process roughly 750,000 words in one go — that's about 10 novels.
Fine-tuning: Training a general AI on specific data to make it better at one thing. Like taking a general doctor and training them as a heart specialist.
RAG (Retrieval-Augmented Generation): Instead of relying only on what the AI "remembers," you connect it to a database. It searches for relevant information first, then uses that to generate more accurate answers. This is how AI avoids hallucination about your company's specific data.
ChatGPT (by OpenAI): The most popular AI chatbot. Does text, images, video, voice, code, browsing. The Swiss Army knife of AI.
Claude (by Anthropic): Best for coding and writing. Known for thoughtful, nuanced responses and strong safety principles.
Gemini (by Google): Built into Google Workspace. Best for reasoning tasks and if you live in the Google ecosystem.
Llama (by Meta): Free and open-source. You can download and run it on your own computer — no subscription needed.
The quality of AI output depends heavily on your input. Three tips that immediately improve results: be specific about what you want (format, length, tone), give context about who you are and why you need it, and provide examples of what good output looks like.
Instead of "write about dogs," try: "Write a 200-word paragraph about why golden retrievers make good family pets, in a warm conversational tone, for a pet adoption website." The difference in output quality is dramatic.