Truncation
Definition
Truncating sequences brings inputs to an allowable length.
Shortening an input sequence to an allowable length according to a defined strategy.
Simply explained
If a text does not fit within the allowed length, parts are omitted according to a specified rule.
Why it matters in practice
The term helps to select, develop, evaluate AI systems in a well-founded manner and realistically assess their technical risks.
Example from business
A ticketing system keeps subject, last message, and relevant metadata within the token limit.
Limits and pitfalls
- Blind truncation can remove evidence or instructions; direction, segment priority, and logging must be defined.
Origin & history
With fixed context windows of neural models, truncation became a standard part of input preparation.
Use in mid-sized companies
- Possible application: A ticketing system keeps subject, last message, and relevant metadata within the token limit.
Frequently asked questions
What does truncating sequences mean?
Shortening an input sequence to an allowable length according to a defined strategy. What should be considered? Blind truncation can remove evidence or instructions; direction, segment priority, and logging must be defined.
Sources
- Padding and truncation · Hugging Face, Padding and truncation
Source checked on 10.10.2026. Editorial application examples are hypothetical; no evidence of real customer results.
Editorially reviewed on: