---
type: concept
title: "Limitations of AI Coding Models"
description: "General coding models stay too general and hallucinate, and silently generate stale library code because of the training cutoff."
tags: [ai-coding, hallucination, training-cutoff, libraries]
videos: [coding-subagents-the-next-evolution-of-ai-ides, chatgpt-users-be-aware-of-this-major-weakness]
created: 2026-07-21
updated: 2026-07-21
---

# Limitations of AI Coding Models

The core limitation of a general-purpose coding model is that it is a generalist: broadly competent, narrowly expert at nothing. As Cole puts it, "AI coding assistants are too General they're the Jack of all trades but also the master of none when it comes to coding with different Frameworks tools and libraries." The instinctive fix, feeding it documentation, is not enough on its own, "it doesn't matter if you give them the documentation for something like a framework that is not enough they're still too General." This is the motivation behind more specialized approaches like the [Subagents Pattern](./subagents-pattern.md) and, in some cases, [Fine-Tuning](./fine-tuning.md).

The second, more insidious limitation is the training cutoff, which makes a model silently generate stale library code. A model will confidently write against an old version of a package with no signal that anything is wrong. In one test, "if I ask it what discord.js version are you using for this application it tells me that it's actually using version 12. so it's not even version 13." The real danger is that the model has no way to know it is outdated: "it didn't tell me that it was outdated because it really doesn't have a way to know." Because the code often still looks plausible, this failure hides until the code breaks against the current library.

Both limitations point to the same conclusion: the model alone is not the product. Reliable AI coding comes from supplying fresh, specific context (up-to-date docs via retrieval, targeted subagents, or a strong harness) rather than trusting the base model's general knowledge. This is why hallucination and staleness are treated here as solvable engineering problems, not fixed properties of the tools.

## Contrasts with

- [Agentic Coding](./agentic-coding.md) - the engineered process that compensates for a general model's weaknesses.

## Related

- [Retrieval-Augmented Generation (RAG)](./rag.md) - feeding current, specific documentation to counter stale training data.
- [Fine-Tuning](./fine-tuning.md) - specializing a model to a framework instead of relying on its generalist knowledge.
- [Subagents Pattern](./subagents-pattern.md) - dedicating focused agents to specific frameworks, tools, and libraries.

## Tools

- [ChatGPT](../entities/tools/chatgpt.md) - the general chat model whose stale-library and version-blindness failures are shown here.

## Sources

- [Coding Subagents - The Next Evolution of AI IDEs](../sources/coding-subagents-the-next-evolution-of-ai-ides.md) - "[0:01:11] AI coding assistants are too General they're the Jack of all trades but also the master of none when it comes to coding with different Frameworks tools and libraries"
- [ChatGPT Users BE AWARE of this Major Weakness](../sources/chatgpt-users-be-aware-of-this-major-weakness.md) - "[0:08:20] it didn't tell me that it was outdated because it really doesn't have a way to know"
