Problem overview

LLMs Limitation № 1: Outdated training data

Why is this a Problem

Modern LLMs are trained on data from old public GitHub repositories, Stack Overflow, legacy code, tutorials, etc.

Some code from these sources was not production-ready and follows best practices for new developments.

LLMs often generate outdated architectural decisions because it saw it many times in the training data. It can suggest deprecated patterns, old development practices, or architecture from 15 years ago.

This creates a problem for modern projects in technical debt, scalability limitations, higher operating cost and creates security vulnerabilities.

What’s the Solution

AI makes writing code faster, but only with a Senior Developer who is well onboarded in the project and who can review and understand every line of code that AI changed or produced before this code can be used for production-ready piece.

A Senior Developer must know what was changed, why it was changed, and what can be affected in other parts of the project because of it.

With amateur developers and AI tools there might be a lot of code generated quickly, but if it reaches production, systems become increasingly difficult to maintain, scale, and support, while sensitive user data may be exposed to significant security and compliance risks.

Enkonix approach takes more time but it gets maintainable code that is hard to break and aligned with ISO 27001 expectations around controlled service delivery and quality.

Proof Links:

How and Why LLMs Use Deprecated APIs in Code Completion? (Empirical Study)
Security Weaknesses of Copilot-Generated Code in GitHub Projects (Empirical Study)
Self-Admitted Technical Debt in LLM Software