Why AI Coding Tools Hallucinate

Artificial intelligence has become remarkably good at writing code.

Modern language models can generate APIs, user interfaces, database schemas, infrastructure configurations, test suites, and entire applications from simple natural language instructions. What once required days of engineering effort can now be accomplished in minutes. The productivity gains are undeniable, and for many developers AI has already become an indispensable part of the software development process.

Yet despite these advances, every experienced engineer who has spent significant time working with AI coding tools has encountered the same frustrating reality.

Sometimes the AI simply makes things up.

It references functions that do not exist. It imports packages that were never installed. It rewrites files that did not need to change. It introduces architectural inconsistencies. It confidently proposes implementations that appear correct on the surface but fail when examined closely.

The industry has given this phenomenon a name:

Hallucination.

But hallucination is only part of the problem.

The deeper issue is something software engineers understand intuitively but AI systems often struggle with: maintaining coherence across a growing codebase over time.

As projects become larger and more complex, AI systems frequently begin to drift away from the architectural reality of the software they are modifying. Small inconsistencies accumulate. Context becomes fragmented. Assumptions compound. Eventually, the generated code may still look convincing, but it no longer reflects the actual state of the system.

At Codira™, we believe this is one of the most important challenges facing AI-native software development.

It is also one of the primary reasons we built a fundamentally different architecture.