A field guide to AI positions

Prompts & prompt engineering

Using AI.

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

Publication dates and source age

Sources counted: 3

Newest dated source: 2022-11-16

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

Prepared with AI assistance. Not independently fact-checked. Original sources and qualifications are below.

In plain language

The instructions and other input given to a model for a task. [1]

Reference this explanation or suggest a correction

Explanation based on the linked sources. Claim ID: claim-term-prompt-1a5192ba3d7825ca26b24a72. Edition 0.20.0.

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Limits & distinctions

Ordinary prompting changes the input while leaving the learned weights unchanged. Instructions alone also cannot enforce which files or services an application may access. [1] [3]

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Explanation based on the linked sources. Claim ID: claim-term-prompt-09422ce4d5c74cb753a9bb98. Edition 0.20.0.

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A fuller explanation

A prompt can contain a question, background material and examples of the desired answer. Prompt engineering means trying and refining that input. For example: 'Summarize this notice in three sentences for a first-time visitor.' [1]

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Explanation based on the linked sources. Claim ID: claim-term-prompt-1e4c26398ee834b2e16dc7b8. Edition 0.20.0.

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How it relates to the map

When comparing outputs, check whether the models received the same instructions and information. [2]

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Editorial interpretation or qualification. Claim ID: claim-term-prompt-25f52a21ad9f2e54a62ed1b2. Edition 0.20.0.

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https://theaiatlas.org/ideas/prompt/

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Sources and what we read

  1. 1. LLMs: Fine-tuning, distillation, and prompt engineering

    Google for Developers · First-hand source · Published date unavailable · Updated 2025-12-03 · Material last read 2026-09-15

    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 scope is described in the source note.

    No archive check recorded.

    Read fine-tuning and prompt engineering, including the distinction between parameter updates and examples supplied as input. Used to distinguish these processes, without adopting general claims that fine-tuning is always necessary or improves every task.

    Source ID: google-llm-tuning

  2. 2. Holistic Evaluation of Language Models

    Percy Liang and coauthors / Stanford CRFM, arXiv · First-hand source · Published 2022-11-16 · Updated 2023-10-01 · Material last read 2026-09-15

    Source is over 18 months old.

    Publication dates and source age

    Sources counted: 1

    Newest dated source: 2022-11-16

    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 scope is described in the source note.

    No archive check recorded.

    Read the abstract and version history, including multiple use cases and metrics, standardized comparisons and acknowledged coverage gaps. Used for evaluation principles; historical model scores are not presented as current rankings.

    Source ID: helm-paper

  3. 3. LLM06:2025 Excessive Agency

    OWASP Gen AI Security Project · First-hand source · Published date unavailable · Material last read 2026-09-15

    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 scope is described in the source note.

    No archive check recorded.

    Read agency definition, excessive functionality/permissions/autonomy, external authorization, approvals and monitoring limits. The 2025 label identifies the edition; the page does not establish its original publication date.

    Source ID: owasp-excessive-agency

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