When an AI's context window is nearly full, don't rely on its automatic compaction feature. Instead, proactively instruct the AI to summarize the current project state into a "process notes" file, then clear the context and have it read the summary to avoid losing key details.
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Human developers may prefer longer files, but AI coding assistants process code in smaller chunks. App developer Terry Lynn intentionally keeps his files small (under 400 lines) to reduce the AI's context window usage, prevent it from getting lost, and improve the speed and accuracy of its code generation.
When an AI coding assistant gets off track, Tim McLear asks it to generate a summary prompt for another AI to take over. This "resume work" prompt forces the AI to consolidate the context and goal. This summary often reveals where the AI misunderstood the request, allowing him to correct the course and restart with a cleaner prompt.
Structure AI context into three layers: a short global file for universal preferences, project-specific files for domain rules, and an indexed library of modular context files (e.g., business details) that the AI only loads when relevant, preventing context window bloat.
Don't try to create a comprehensive "memory" for your AI in one sitting. Instead, adopt a simple rule: whenever you find yourself explaining context to the AI, stop and immediately have it capture that information in a permanent context file. This makes personalization far more manageable.
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.
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.
Instead of manually rereading notes to regain context after a break, instruct a context-aware AI to summarize your own recent progress. This acts as a personalized briefing, dramatically reducing the friction of re-engaging with complex, multi-day projects like coding or writing.
When a conversation with Codex approaches its context window limit, using `/new` erases all history. The `/compact` command is a better alternative. It instructs the LLM to summarize the current conversation into a shorter form, freeing up tokens while retaining essential context for continued work.
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.
