What is a context window?
A context window is the amount of text a language model can look at and use when generating new responses, including the text it creates. It is different from the data the model was trained on and acts like the model’s working memory. A larger context window helps the model handle longer prompts and maintain coherence in extended conversations, while a smaller window can limit its understanding and responses.
When the context window approaches its limit, older messages may be summarized to make room for new input. This ensures the model retains essential context without losing the continuity of the conversation. The problem with the summarizing approach is that context is inevitably lost. As previous messages are condensed, subtle details, nuances, and important qualifiers may disappear. Over time, this can reduce the model’s response accuracy, lead to minor misunderstandings, or even cause it to make incorrect assumptions based on incomplete information.
Long context windows don’t fully replace good chat hygiene
Bigger context windows allow you to feed the model more information at once, but they do not eliminate other risks:
Memory is still limited
Even with a one million or two million token limit, models may still drop or truncate older conversation bits when the limit is reached. Other issues, like sycophancy and hallucinations, mean we still need to be careful with how we use AI models in our workflows.
Sycophancy: when the AI just agrees with you
Large language models, shaped by their training, often lean toward agreeing with user prompts, even subtly inaccurate ones. This tendency is known as sycophancy and can introduce risk. The AI may confirm mistaken facts or assumptions, leading to confident but incorrect outputs in campaign briefs or strategy documents. Over time, especially in long single-session chats, mistakes may compound without being noticed.
Conclusion
Even with large context windows, AI models are not perfect memory tools. They can summarize, forget, or misinterpret information, and they are prone to sycophancy and hallucinations. For this reason, it is important to approach AI outputs with care. Always double-check facts, review recommendations critically, and consider restarting chats when needed to keep interactions clear and manageable.
References:
Long context (Google).
Context windows (Anthropic).