OpenAI opened ChatGPT to public testing to learn how people use a dialogue-focused AI system and gather feedback for future improvements. The chatbot was built to respond in conversation, but its release also made visible the limits of training an AI to sound helpful: fluent answers could still be wrong, repetitive, or inconsistent.
Human feedback shaped the conversation model
ChatGPT was developed using methods similar to those used for InstructGPT, with an additional focus on dialogue. Human trainers wrote both sides of sample conversations: the user's message and the assistant's reply. They could draw on model-generated suggestions while preparing those examples.
OpenAI also used conversations between trainers and the chatbot to build a reward model. Trainers compared or rated AI-generated responses, and those ratings helped guide further training. The model was fine-tuned with proximal policy optimization, and OpenAI repeated the process multiple times.
This approach is known as reinforcement learning from human feedback, or RLHF. The aim is to make responses that people evaluate more favorably and to reduce problems such as hate speech and misinformation. The article describes ChatGPT's base as a model from the GPT-3.5 series, whose training finished in early 2022. The models were trained on Microsoft's Azure AI platform.
Fluent answers were not always reliable
A central limitation was that ChatGPT could produce responses that sounded plausible while being incorrect or nonsensical. Making a system more conversational does not, on its own, guarantee that what it says is true. OpenAI said this was difficult to address because there is no single source of truth for every question.
Training choices could create other trade-offs. A model taught to be cautious might refuse questions it could have answered. In supervised training, what counts as an ideal reply can depend on what the model already knows, rather than only on the human demonstrator's response.
The chatbot could also react differently to small changes in wording. A slight rephrasing might lead it to answer correctly, answer incorrectly, or not answer at all. That variability matters for anyone relying on a conversational system: the same underlying question may not get a consistent response when phrased another way.
Helpfulness could become verbosity or guesswork
OpenAI said ChatGPT could be too wordy, repeat itself, and lean on familiar phrases. The article links this behavior to over-optimization and to human instructors preferring more detailed answers during feedback. In other words, a training signal favoring detail could encourage long responses even when a shorter one would be clearer.
When a user's intent was unclear, ChatGPT tended to guess instead of asking a follow-up question. It could also respond to inappropriate requests rather than refuse them. OpenAI said it was using its moderation API to reject requests that violated its content policies, while acknowledging that limitations remained.
These issues help explain why an open test can be useful: real users can reveal failure cases that trainers may not anticipate. OpenAI said it planned regular model updates and hoped the accessible interface would bring feedback about problems not already known.
A test interface for a larger ambition
ChatGPT was available free to people with an OpenAI account. Sam Altman, co-founder of OpenAI, described it as an “early demo of what's possible.” He also suggested that language interfaces could become an important way for people to interact with computers.
The launch sat alongside other experiments in conversational AI. Deepmind had introduced Sparrow, which was trained with human feedback and had internet access to research and verify current information. Deepmind viewed it as a foundation for more advanced assistants but chose not to release it for security reasons. Google LaMDA was rolling out in a test environment.
ChatGPT's public test offered a practical way to examine both the promise and the rough edges of conversational AI. Its responses could feel natural, but users still had to account for errors, inconsistency, overlong answers, and weak handling of ambiguity. The feedback process was part of the experiment: observing those shortcomings could guide later improvements.