What Happened
A detailed complaint on Reddit's r/ChatGPT on March 7, 2026 laid out a problem that many daily users have been feeling: ChatGPT's responses have become bloated with filler text to the point where actually getting useful information requires real effort.
The user described how "any short, simple thing I ask of ChatGPT has to be answered with a wall of text that is 80% useless words for engagement and 20% the information I seek." For more complex prompts, the situation is worse - the model interrupts itself, hedges extensively, withholds direct answers, and forces users through several rounds of follow-up prompts to extract what should have been in the first response.
The post also called out excessive political correctness, noting that anything "slightly off center" of mainstream topics triggers layers of disclaimers and caveats that further pad responses without adding value.
This thread isn't an outlier. Search r/ChatGPT for "verbose" or "too long" and you'll find months of similar complaints. Users have developed their own workarounds - adding "be concise" to every prompt, using custom instructions that demand brevity, or switching to system prompts that explicitly ban filler phrases. The fact that an entire subculture of anti-verbosity prompting exists tells you how widespread the problem is.
Why It Matters
The core value of an AI assistant is saving time. When you have to read through five paragraphs of hedging to find a two-sentence answer, then send follow-up prompts to strip away the padding, the time savings evaporate. For power users running dozens of queries per day, this overhead adds up fast.
The verbosity hits hardest in professional workflows. A developer asking for a quick code fix gets a lecture on best practices they didn't ask for. A marketer asking for three headline options gets ten, each with explanatory paragraphs. A researcher asking for a summary gets something longer than the source material.
There's also a token cost angle for API users. Every unnecessary word in a response costs money. If 80% of output tokens are filler, as the Reddit post claims, API users are paying roughly four times more than they should for the actual information content.
Our Take
Verbosity in language models isn't accidental. Models are trained on human feedback, and longer, more detailed responses tend to get rated higher in preference evaluations - even when shorter answers would serve the user better. This creates a systematic bias toward padding.
OpenAI knows this is a problem. They've shipped features like "concise mode" and response length controls in some interfaces. But the default experience - which is what most users encounter - remains tuned for verbosity. And defaults matter more than settings buried in menus.
The practical question for daily users is whether it's worth fighting the model's defaults or switching to something that respects your time out of the box. Claude tends to be more calibrated on response length, giving proportional answers - short questions get short answers without the preamble. Gemini has also tightened up in recent months.
If you're sticking with ChatGPT, the most effective fix we've found is setting custom instructions that say: "Be direct. No preamble. No summaries unless asked. Match response length to question complexity." This cuts the bloat significantly. But you shouldn't need a custom prompt to get a concise answer to a simple question - that should be the baseline.