Author(s): Tina Sharma

Originally published on Towards AI.

The gap between people who follow AI and people who don’t isn’t intelligence. It’s 21 words.

Here is something that happens a lot. You open an article about a new AI model. The first paragraph mentions that it was “fine-tuned on domain-specific data using a masked language modeling objective with RAG for retrieval.” You either pretend to understand this, close the tab, or Google each term separately and lose the thread entirely.

The article argues that most AI terms aren’t inherently hard, but are rarely explained—and gives a practical “decoder” of common vocabulary. After introducing the need for shared language, it walks through how models are built and used: tokens (the unit that drives cost and limits), parameters and weights (the learned values that define model capacity and versions like open vs. proprietary weights), training vs. inference, and context windows (working memory measured in tokens). It then covers how models learn (training objectives/loss, overfitting, and benchmarks), and shifts to language-model concepts like LLMs, embeddings/vectors, and transformers (including attention and self-attention). Finally, it explains output controls and failure modes such as temperature and hallucination, the role of prompt engineering (zero-shot vs. few-shot), and systems built on top of models like RAG and knowledge distillation. It concludes by reframing the value of the vocabulary: you can interpret claims, spot hype, and understand what those terms imply about real-world usefulness—so AI conversations stop being confusing and start being meaningful.

Read the full blog for free on Medium.

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Published via Towards AI