AI models like Claude Code can experience a decline in output quality as their context window fills. It is recommended to start a new session once the context usage exceeds 50% to avoid this degradation, which can manifest as the model 'forgetting' earlier instructions.

Continuously trying to correct a confused AI in a long conversation is often futile, as a 'poisoned' context can lead it astray. The most effective approach is to abandon the conversation, start a new one, and incorporate your learnings into a better initial prompt.

When an AI model gives nonsensical responses after a long conversation, its context window is likely full. Instead of trying to correct it, reset the context. For prototypes, fork the design to start a new session. For chats, ask the AI to summarize the conversation, then start a new chat with that summary.

Even models with million-token context windows suffer from "context rot" when overloaded with information. Performance degrades as the model struggles to find the signal in the noise. Effective context engineering requires precision, packing the window with only the exact data needed.

Long, continuous AI chat threads degrade output quality as the context window fills up, making it harder for the model to recall early details. To maintain high-quality results, treat each discrete feature or task as a new chat, ensuring the agent has a clean, focused context for each job.

To avoid context drift in long AI sessions, create temporary, task-based agents with specialized roles. Use these agents as checkpoints to review outputs from previous steps and make key decisions, ensuring higher-quality results and preventing error propagation.

Contrary to intuition, providing AI with excessive or irrelevant information confuses it and diminishes the quality of its output. This phenomenon, called 'context rot,' means users must provide clean, concise, and highly relevant data to get the best results, rather than simply dumping everything in.

Long-running AI agent conversations degrade in quality as the context window fills. The best engineers combat this with "intentional compaction": they direct the agent to summarize its progress into a clean markdown file, then start a fresh session using that summary as the new, clean input. This is like rebooting the agent's short-term memory.

Long conversations degrade LLM performance as attention gets clogged with irrelevant details. An expert workflow is to stop, ask the model to summarize the key points of the discussion, and then start a fresh chat with that summary as the initial prompt. This keeps the context clean and the model on track.