An AI Glossary

06/24/25
Source: Lenny Rachitsky. Read the original article

TL;DR Summary of The Top 20+ Most Common AI Terms Explained, Simply

This guide breaks down **20+ essential AI terms** such as LLM, transformers, and hallucination into easy-to-understand language. It explains the differences between **pre-training, fine-tuning, and RLHF**, and clarifies concepts like **prompt engineering** and **RAG** for improving AI outputs. The article also covers foundational ideas including **AGI vs ASI**, why models hallucinate, and the importance of **synthetic data**. Readers gain practical knowledge to confidently discuss and utilize AI technology.

Optimixed’s Overview: Demystifying Key AI Concepts for Better Understanding and Application

Understanding AI Models and Their Development

The article starts by defining what an AI model is and outlines the crucial stages of its development: pre-training, fine-tuning, and reinforcement learning with human feedback (RLHF). These terms describe how AI systems learn from data and improve over time.

Transformers and Their Impact

It explains the role of transformers, a breakthrough AI architecture that revolutionized natural language processing by enabling models to efficiently understand context and sequence in data. This technology underpins many modern AI systems.

Enhancing AI Outputs

The guide covers techniques like prompt engineering and retrieval-augmented generation (RAG), which help refine AI responses, making them more accurate and relevant. Understanding these tools is essential for optimizing AI interactions.

Clarifying Common AI Terms and Phenomena

  • LLM (Large Language Model) vs. GenAI vs. GPT: Differentiating types of AI models and technologies.
  • Hallucination: Why AI models sometimes generate incorrect or nonsensical outputs, and strategies to reduce this issue.
  • Synthetic data: Its role in training AI models when real data is scarce or sensitive.
  • Additional concepts like vibe coding, agents, MCP, inference, and tokens are explained in accessible terms to broaden understanding.

Overall, this comprehensive explanation equips readers with the foundational vocabulary and insights needed to engage confidently with AI technologies and their applications.