A field guide to AI positions

Embeddings

Dated sources are over 18 months old; other dates are unknown.

Publication dates and source age

Sources counted: 2

Newest dated source: 2020-05-22

Assessed at this edition's evidence cutoff: 2026-09-15. 18-month boundary: 2025-03-15.

Some publication dates are unknown; the newest dated source may not be the newest source overall.

Publication age does not tell us whether a claim is still valid. Reading an old source again does not make its publication date newer. An update date does not establish that the passage we used was updated.

Open in the glossary Reading notes · Structured record

In plain language

Lists of numbers that represent information in a form a model can compare or process. [1]

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Limits & distinctions

Similarity depends on the model and task. Nearby representations do not establish that two statements mean exactly the same thing or are true. [1]

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A fuller explanation

An embedding represents something, such as a word or passage, as a position in a mathematical space. Items represented nearby can be similar for the task the model learned. Search systems can use such comparisons to find related passages. [1] [2]

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How it relates to the map

This helps explain how a system can look for related content beyond exact word matches. [1] [2]

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https://theaiatlas.org/ideas/embedding/

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Image previews are summaries. Keep the page link so readers can check the evidence.

Sources and what we read

  1. 1. Embeddings: Embedding space and static embeddings

    Publication dates and source age

    Sources counted: 1

    Publication dates are unavailable.

    Assessed at this edition's evidence cutoff: 2026-09-15. 18-month boundary: 2025-03-15.

    Some publication dates are unknown; the newest dated source may not be the newest source overall.

    Publication age does not tell us whether a claim is still valid. Reading an old source again does not make its publication date newer. An update date does not establish that the passage we used was updated.

    Read numerical representations, distance as relative similarity, task dependence and the limits of human-readable dimensions. The food diagrams are teaching examples, not measurements reused in this atlas.

  2. 2. Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks

    Source is over 18 months old.

    Publication dates and source age

    Sources counted: 1

    Newest dated source: 2020-05-22

    Assessed at this edition's evidence cutoff: 2026-09-15. 18-month boundary: 2025-03-15.

    Publication age does not tell us whether a claim is still valid. Reading an old source again does not make its publication date newer. An update date does not establish that the passage we used was updated.

    Read abstract and version history, plus version 4's results sections 4.3/4.4 and Broader Impact discussion during the history review. External passages can contain errors or bias. Used for the original approach; benchmark results do not establish accuracy for every system now called RAG.

Edition and machine-readable evidence

Content version 0.20.0. Evidence cutoff 2026-09-15; this does not mean every source was read on that day.

Pinned complete dataset · Complete evidence page · Agent consumption guide