A city-council minutes chatbot appends every past turn until the endpoint returns a length error. Where is the fix?
Select an answer to reveal the explanation.
Short Explanation
The minutes bot keeps stuffing every past turn until the endpoint chokes on length. Trim oldest turns or summarize history in the client so the next request fits the context window. Fine-tuning a new model, or calling it a training-topology issue, is not the client fix.
Full Explanation
A model's context window is a hard length limit on the packed request. When a minutes chatbot appends every past turn, the client must trim oldest turns or summarize history so the next call fits. Fine-tuning a new model each time the window fills is not the client fix. Distributed-training topology is a different problem.