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AI Concepts & Terminology

Learn the vocabulary of modern AI, from models, parameters, and tokens to training, inference, and hallucination, so technical articles and product documentation stop feeling like a foreign language.

Material Overview
AI Concepts & Terminology

Work through the terms that appear in every AI article, product page, and research summary. You will learn what models, datasets, parameters, weights, tokens, embeddings, and context windows really mean, and how training, fine tuning, and inference differ from one another. Every concept is paired with a plain language example and a common misuse, so you finish able to read AI documentation, follow product announcements, and ask sharper questions in meetings.

Curriculum

Learning Path

Each level is enrolled separately. Work through them in order, or start at the level that matches your experience.

  1. Level 01

    Beginner

    Purpose

    Learn the core vocabulary that appears in every AI article and product page so you can follow the conversation without guessing.

    Outcome

    You can read a mainstream AI article and understand every term in it.

    What You'll Learn
    • Understand model, dataset, algorithm, training, and prediction in plain language
    • Separate the terms artificial intelligence, machine learning, and deep learning and see how they nest
    • Recognise the labels vendors attach to products and what each one really promises

    294 CREDS

  2. Level 02

    Intermediate

    Purpose

    Move into the terms used inside model documentation, where parameters, tokens, and context windows decide what a system can actually do.

    Outcome

    You can read a model card or API reference and know what its numbers mean for your use case.

    What You'll Learn
    • Learn what parameters and weights represent and why model size is quoted in billions
    • Understand tokens, context windows, and how they limit the length of a conversation
    • Compare embeddings and vectors and see how meaning becomes numbers

    415 CREDS

  3. Level 03

    Advanced

    Purpose

    Learn the operational vocabulary of teams that build with AI, where the difference between fine tuning, retrieval, and prompting changes the budget.

    Outcome

    You can join a technical planning discussion and use the terms correctly in the right context.

    What You'll Learn
    • Distinguish pretraining, fine tuning, and inference, and see who pays for each
    • Learn retrieval, grounding, agents, and tool calling as the terms are used in practice
    • Understand latency, throughput, and cost per token as the constraints they really are

    691 CREDS

  4. Level 04

    Expert

    Purpose

    Read research and evaluation language critically so benchmark scores and safety claims stop being taken at face value.

    Outcome

    You can read a model release or research summary and separate a real result from marketing.

    What You'll Learn
    • Interpret benchmark names, accuracy scores, and the conditions that make them meaningless
    • Learn evaluation terms such as ground truth, baseline, ablation, and statistical significance
    • Decode safety vocabulary including alignment, red teaming, guardrails, and jailbreaks

    964 CREDS