Guess Who Gets Blamed When AI Coding Assistants Hallucinate

Software devs are discovering something we tech writers have been dealing with for years: confidently wrong info is still wrong info.

The difference is that now the errors — hallucinations — compile.

A recent post on DEV Community titled “Copilot is gaslighting developers and we’re all pretending it’s fine” captured something a lot of engineers have been quietly muttering in Slack channels, Reddit threads, and late-night debugging sessions: AI coding assistants routinely invent APIs, misunderstand context, generate brittle logic, and produce code that looks correct while quietly introducing defects.

Because once people trust the machine, they stop questioning it. And when software devs stop questioning auto-generated code, documentation suddenly becomes far more important than in days gone by.

Related Reading: When AI Gets It Wrong: Addressing AI Hallucinations and Bias (MIT Sloan Teaching & Learning Technologies)

Hallucinations Don’t Just Happen In Chatbots

We’ve spent the last two years talking about AI hallucinations mostly in the context of chat interfaces. Lawyers have been chastised by judges for submitting fake case citations conjured up by genAI. Students and researchers have used AI tools that generate fabricated academic references, a documented problem in academic integrity and scholarly publishing. Executives pasting strategy documents into systems that respond with polished nonsense.

But developers are now running into the same phenomenon inside their Integrated Development Environments (IDEs). GitHub Copilot, Cursor, Claude Code, and similar tools can generate useful boilerplate quickly. They can also fabricate nonexistent methods, misuse libraries, recommend outdated syntax, or confidently apply patterns inappropriate for a specific architecture.

The dangerous part isn’t that the code is always terrible.; it’s that it’s often plausible.

Plausible code is seductive. It reads cleanly. It appears intentional. It creates the illusion that someone smart already thought through the problem. Researchers studying AI-assisted programming found developers reviewing AI-generated code missed significantly more bugs than those reviewing human-written code because the generated code appeared polished and trustworthy.

That should sound familiar to tech writers. We’ve been warning people for decades that clarity is not the same thing as correctness.

The Tech Docs Connection Nobody Wants To Talk About

AI coding systems are trained on code repositories, documentation, tutorials, Stack Overflow posts, API references, GitHub examples, forum comments, and whatever random developer named Chad uploaded at 2:00 a.m. after three energy drinks and an emotional support burrito. 🌯

In other words, AI systems consume the same messy info humans do. If our technical documentation is ambiguous, outdated, inconsistent, incomplete, or lacking context, the AI inherits those problems and amplifies them at machine speed. This means bad docs no longer merely confuse humans; now they contaminate machine-generated output. This is a huge shift.

For years, the organizations we served treated docs as secondary support material. Helpful, but not operationally critical. Something to “clean up later” — maybe — or to leave to devs to “just figure out.” AI changes that equation.

Today our docs are raw materials for training, retrieval, grounding, and behavioral instruction for machines generating code. When a coding assistant generates a nonexistent API parameter, oftentimes the problem isn’t the model. Instead, the docs likely failed to clearly distinguish deprecated functionality, edge cases, conditions, actor responsibilities, or supported workflows.

Machines are exposing weaknesses in our documentation architecture the same way a blacklight exposes stains on hotel bedspreads 🦠. Yuck!

Suddenly everybody’s pretending not to notice what’s been there the whole time.