---
type: concept
title: "Training Cutoff"
description: "An AI model's knowledge is frozen at the date its training data ends, so it silently knows nothing about events, libraries, or versions released afterward."
tags: [llms, training-cutoff, model-limitations]
videos: [chatgpt-users-be-aware-of-this-major-weakness]
created: 2026-07-21
updated: 2026-07-21
---

# Training Cutoff

A training cutoff is the date at which a model's training data stops. Everything after it is simply absent from the weights. Ask the model itself and it will tell you: "what is your training cut off and it says it is September of 2021 and explains what that means is it's trained on data from September 2021 and earlier" [0:00:48]. Cole frames the consequence as the defining limitation of a raw LLM: "the AI model is stuck at a point in time where it's trained it has all the past knowledge but nothing in the future and so this is the biggest weakness" [0:01:03].

What makes the cutoff dangerous is that it is silent. The model does not return "unknown" for a library released last month; it confabulates plausible APIs from the older versions it does know, which is why cutoff drift is a leading cause of hallucinated function signatures and deprecated patterns in generated code. The failure is invisible precisely because the answer looks fluent.

Every practical remedy is the same move: put the missing information into the context window at inference time instead of hoping it lives in the weights. That is the entire argument for [Retrieval-Augmented Generation (RAG)](./rag.md), for feeding current library docs to a coding assistant, and for giving agents live search and crawling tools. [Fine-Tuning](./fine-tuning.md) can also push new knowledge into a model, but it re-freezes at a new date, so it solves style and behavior far better than it solves freshness. The durable design rule is to treat the model as a reasoning engine over supplied context, never as an up-to-date database.

## Builds on

- [Parametric Knowledge](./parametric-knowledge.md) - what a model knows from its weights alone is exactly what the cutoff bounds.

## Contrasts with

- [Retrieval-Augmented Generation (RAG)](./rag.md) - retrieval supplies post-cutoff facts at inference time instead of relying on frozen weights.
- [Fine-Tuning](./fine-tuning.md) - retraining moves the cutoff forward but does not remove it.

## Related

- [Hallucination Detection](./hallucination-detection.md) - post-cutoff APIs are a prime source of confident, wrong output.
- [Docs and Templates as Coding-Assistant Context](./docs-as-agent-context.md) - loading current documentation is the everyday fix for coding agents.
- [llms.txt](./llms-txt.md) - a doc format built so agents can pull current library knowledge on demand.
- [Web Crawling & Ingestion](./web-crawling.md) and [Knowledge Bases](./knowledge-bases.md) - how fresh material gets in front of the model.
- [Agentic Search](./agentic-search.md) - letting the agent go fetch what it does not know.
- [Model Selection](./model-selection.md) - newer models ship later cutoffs, which matters for fast-moving stacks.
- [Context Engineering](./context-engineering.md) - the discipline of supplying exactly the knowledge the weights lack.

## Tools

- [ChatGPT](../entities/tools/chatgpt.md) - the model Cole interrogates about its own cutoff in the video.

## Sources

- [ChatGPT Users BE AWARE of this Major Weakness](../sources/chatgpt-users-be-aware-of-this-major-weakness.md) - "[0:00:48] what is your training cut off and it says it is September of 2021 and explains what that means is it's trained on data from September 2021 and earlier"
- [ChatGPT Users BE AWARE of this Major Weakness](../sources/chatgpt-users-be-aware-of-this-major-weakness.md) - "[0:01:03] the AI model is stuck at a point in time where it's trained it has all the past knowledge but nothing in the future and so this is the biggest weakness"
