Demystifying AI Terms for Developers

If you’re a developer (like me, coming from PHP and web apps), stepping into the world of AI can feel like drinking from a firehose.
There are too many new words — some are company names, some are research concepts, some are tools you can actually install.

This post is my attempt to make sense of the most common terms I kept bumping into. Hopefully it helps you too.

🧠 Core AI & LLM Concepts

LLM (Large Language Model)

A type of AI trained on huge amounts of text to understand and generate language. Examples: GPT, Claude, Gemini, DeepSeek.

Transformers

The deep learning architecture that powers modern LLMs. Instead of reading text word by word, it looks at relationships between tokens all at once. This makes models like GPT possible.

Tokenization

Before an LLM can read text, it breaks it down into tokens (pieces of words). Example: "PHP rocks!" might become [PHP] [rocks] [!].
Important because tokens affect cost, speed, and memory.

Context Window

How much text (in tokens) an LLM can “remember” at once.
Example: GPT-4 might handle 128k tokens (~100,000 words). If you exceed that, older parts get forgotten.

Embeddings

A way to represent text as numbers (vectors) that capture meaning. Example: “dog” and “puppy” end up close to each other in vector space.

Vector Database

A database optimized for storing and searching embeddings. Used to quickly find semantically similar text. Examples: Pinecone, Weaviate, Milvus.

RAG (Retrieval-Augmented Generation)

A technique where you feed extra knowledge into an LLM by retrieving relevant docs from a vector database, instead of making the model memorize everything.

Fine Tuning

Adjusting a pre-trained LLM with your own dataset. Good for domain-specific knowledge (like law or medical) or tone.

Multi-Modality

When an AI can process not just text, but also images, audio, or video. Example: GPT-4 with Vision, or Gemini handling text + images + code.

Why LLM Needs GPU

GPUs are specialized at parallel computation (matrix multiplications). LLMs are huge, so CPUs would be way too slow.

Entropy — Balancing Randomness and Determinism

When generating text, entropy (via temperature/top-p sampling) controls randomness.

• Low randomness → more predictable but boring answers.
• High randomness → more creative but sometimes nonsense.

⚡ AI Tools & Companies

GPT

OpenAI’s flagship LLM family. ChatGPT is the app version, GPT is the model itself.

Claude

Anthropic’s family of LLMs. Known for being careful, safe, and conversational.

Gemini

Google DeepMind’s family of multimodal AI models. Strong integration with Google products.

DeepSeek

An emerging open-source LLM from China, optimized for speed and efficiency.

Ollama

A tool for running open-source LLMs locally on your laptop with one command. Example:

ollama run llama3

LLaMA

Meta’s open-source LLM (different versions like LLaMA 2, LLaMA 3). Forms the base for many community projects.

Anthropic

The company behind Claude, founded by ex-OpenAI employees. Focused on “constitutional AI” (aligning models with human values).

n8n

An open-source workflow automation tool (like Zapier) that can integrate LLMs, APIs, and databases into pipelines.

Lovable

A YC-backed startup that lets you generate full-stack apps with natural language prompts. Think of it as “AI + coding copilot” on steroids.

LangChain

A developer framework (Python/JS) for building AI apps. It helps you connect LLMs with tools, manage RAG pipelines, and build AI agents that can take actions.
Think of it as a framework layer that makes LLMs practical in real-world applications.

Hugging Face

An open-source AI hub and community. It provides:

• Model Hub (download/share models like LLaMA, Mistral, Stable Diffusion)
• Transformers library (Python package for using models)
• Datasets Hub (shared datasets for training/fine-tuning)
• Spaces (hosted demos and apps)

Think of it as the GitHub of AI models and tools.

🌟 New AI Trends

Agentic AI

Instead of just answering, an AI acts like an agent — can plan, call APIs, run tools, and take actions autonomously.

Vibe Coding

Letting the AI generate code in an exploratory, playful way — where you don’t always know the end result, but you’re “vibing” with the model as a creative partner.

🚀 Final Thoughts

AI terms can feel overwhelming at first, but here’s the good news:
You don’t need to learn everything at once. Start with LLMs + embeddings + RAG, because they’re the building blocks of most real-world AI apps today.

Then, experiment with Ollama locally, or try integrating n8n workflows.
Before you know it, you’ll be shipping AI features like you ship PHP apps.

Did I miss a term that confused you? Drop me a comment and I’ll add it to the list.

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